The enormous expansion of the video game sector, driven by the emergence of live video game streaming platforms and the professionalisation of this hobby through e-sports, has spurred interest in research on the relationships with potential adverse effects derived from cumulative use. This study explores the co-occurrence of the consumption and viewing of video games, based on an analysis of the motivations for using these services, the perceived positive uses, and the gamer profile. To that end, a multilayer perceptron artificial neural network is developed and tested on a sample of 970 video game users. The results show that the variables with a significant influence on pathological gaming are the motivation of a sense of belonging to the different platforms, as well as the positive uses relating to making friends and the possibility of making this hobby a profession. Furthermore, the individual effects of each of the variables have been estimated. The results indicate that the social component linked to the positive perception of making new friends and the self-perceived level as a gamer have been identified as possible predictors, when it comes to a clinical assessment of the adverse effects. Conversely, the variables age and following specific streamers are found to play a role in reducing potential negative effects. The video game has overcome earlier stereotypes3; it is no longer just a simple form of amusement designed especially for children, young people or teenagers6. Indeed, there is no end to the growth in the applications and uses of video games9. They have become part of the everyday life of millions of users10. Forecasts suggest that by 2023 they will play some kind of role in the lives of two-thirds of the world's population11. On top of all this, there is the recent emergence of new phenomena that have revolutionised how people enjoy video games: the rise of the professionalisation of this hobby through e-sports15, and watching video games on live streaming platforms18. These activities are interrelated on platforms such as Twitch19, where users have a multitude of ways to enjoy and share their hobby, whether actively—by streaming their own experiences21—or passively19, for example, by watching others play to observe their skills, following competitions and events, sharing experiences in chats, and learning the ins and outs of old games or new releases23. This aspect of the activity, which is far more social, is accessible to almost anyone with an internet connection19.Over time, video games have become one of the most influential, commercially viable and appealing entertainment media available to society28, which is also found on streaming platforms31: pathological use that could be linked to the abuse of video games, the internet and live streaming. Among others, it is worth mentioning aspects related to excessive gaming33, bad online behaviour, harassment or dissemination of inappropriate content36. Since the 1990s, there has been a growing and extremely complex debate centred on the underlying notion of groups of people who lose control of their behaviour and experience pathological symptoms that affect their daily lives37. Recently, a prominent core of the research has focused on the effects of co-occurrence of different activities. Studies such as those by Reer et al.37, Sussman, et al.38 and Andreassen et al.39 reveal the feedback between and co-occurrence of adverse forms of behaviour stemming jointly from the internet, social networks and video games. Nevertheless, despite the growing literature on live streaming services40, no attempt has thus far been made to identify the elements that are most likely to trigger potential pathological use stemming from the consumption of video games through viewing.However, there is a less convivial side associated with video games42. To this end, we review the theoretical background related to the aforementioned independent variables, and develop a multilayer perceptron (MLP) artificial neural network (ANN)43. The proposed network is generated from a sample of 970 video game users in Spain, just before the Covid-19 lockdown.The present study seeks to fill this gap and explore the antecedents of pathological gaming stemming from activities related to live streaming platforms; that is, it examines disruptive use derived from the co-occurrence of video game consumption and viewing. Specifically, the focus is on the motivations for using services of this type, positive perceptions of video game consumption, and characteristics linked to the user's profile as a gamer and viewer . Furthermore, we analyse inappropriate behaviour linked to making hurtful comments to other users46. Among the most salient results, it was found that positive perceptions related to the social component of video games have an impact on three of the indicators used by Andreassen et al.39 to measure addiction or pathological use: playing or watching video games to forget about problems, feeling bad if you cannot play or watch video games, and spending more and more time playing or watching video games. At the same time, the inverse effect of age on the dependent variables used in the proposed neural network was also highlighted, indicating that, as age increases, most of the indicators of pathological use would be reduced. This would be particularly significant in the context of the existing debate on gaming as a pathology48, as it suggests that some effects may be circumstantial and tend to diminish as cognitive control over behaviour increases49.In terms of scope, this paper contributes to the existing literature on the controversial balance between the positive side of video game use and the concern for a counterpoint of pathological components. In this context, this paper stands as one of the first studies to explore excessive use derived from the overlapping of gaming and viewing activities, since previous research has mainly focused on analysing positive or negative uses without considering the co-occurrence of these two closely related activities. From a methodological perspective, the use of artificial neural networks to establish usage patterns contributes to provide a more holistic comprehension, and opens new perspectives to understand the importance of the variables used and to consider future causality analyses, for example, based on structural equations modelling, or experimental design51. This type of service offers its users a space to watch content or to live stream, and millions of people connect every day to watch others play or stream themselves52. The ever-growing audience has access to search engines to select channels according to their preferences, typically seeking out a particular streamer or a specific video game53. This new way of experiencing video games encompasses most of the benefits attributed to them, but it also brings with it some of the possible adverse effects related to online video games31. These factors can often trigger functional impairment, loss of productivity, poor academic performance, mood disorders and anxiety55.Streaming platforms, with Twitch leading the field, constitute a novel hybrid of a communication media and a vast social network, based primarily around video games58. However, all this requires a significant increase in the time spent on their hobby, now divided between viewing and actually gaming11.When video game enthusiasts start using live streaming platforms, they enjoy new possibilities to improve their experience with video games, better their skills and abilities by watching others play, expand their network of friends, and even professionalise their hobby59. Among the most prominent are those related to learning60, social support62, improved attention span, cognitive enhancement and creativity65. Regarding live streaming, benefits leading to educational, economic, employment or inclusion opportunities have also been found67. Some recent examples can be observed in studies such as those by Formosa et al.68 and de Wit et al.69 which show how playing and watching video games are configured as a hobby with the capacity to help manage difficult moments in life. However, despite the many positive applications of the range of activities focused on video games, there are concerns, the most important being those associated with a minority of users who may experience transient or persistent symptoms traditionally associated with pathological gaming, for example, changes in mood, tolerance or salience70. In this regard, Chen and Chang31 note how the inclusion of online gaming addiction in the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders as a tentative disorder (DSM); and the admission by the World Health Organization71 of gaming disorder in the International Classification of Diseases were a flashpoint for debate about possible adverse effects. In fact, some authors have expressed concern about the possible stigmatisation of millions of people who use gaming as a healthy hobby, leading to a critical current that warns about the medical, scientific, public health, and social consequences of formalising a disorder in a perhaps premature manner48.Numerous positive effects linked to the use of video games have been described in the field of video games research72 in a recent survey to 214 academics reported that approximately 31% of the researchers participating in their study were sceptical about pathological gaming as a mental health problem. Furthermore, they stated that almost half of them did not believe in the effectiveness of the established criteria for diagnosis . An additional problem involves the terminology employed in the empirical research conducted, as there is no general agreement when referring to possible adverse effects, with different terms used to describe very similar concepts 73. A reasonable criticism of current diagnostic approaches lies in the fact that high commitment to an activity, repetitive and entailing prioritisation of activities, is neither problematic in itself, nor necessarily linked to adverse consequences74, such as a person who is very committed to sport, cinema or any other hobby in which he or she is particularly involved. In any case, this work aligns with the reasoning of Ferguson (2020) who identifies four points of convergence between the two positions: some individuals may experience gaming excesses; video game problems are infrequent; video game disorders are often accompanied by other types of disorders ; and finally, research on adverse effects is valuable, as it is necessary to further knowledge about the interrelationships between the main variables involved in understanding a phenomenon as multifaceted as video games.Ferguson and Colwell75. Different meanings are attached to similar phenomena70, all of which are linked to negative effects that persist for at least a year, compulsive or unhealthy use of digital media78, and disordered80, addictive81, or excessive82 use. It should be noted that, unlike internet gaming disorder (IGD), problematic social media use (PSU) has not yet been recognised as a disorder by the WHO or in the APA manual83. Streaming platforms such as Twitch combine the features of a vast social network with live video game content19. Thus, according to Reer et al.37, PSU would include outcomes similar to those defined in section III of the fifth Diagnostic and Statistical Manual of Mental Disorders (DSM-5) for IGD: preoccupation and excessive connection; time spent gaming at the expense of other activities such as studying, work, relationships and health; and symptoms such as addiction, loss of control and continued use despite experiencing negative consequences84. The concept of problematic use or viewing refers to the user's inability to control the hours spent on (non-work-related) online activities, signs of withdrawal, use to escape social or family problems, and conflicts with the surrounding environment when abandoning other activities31. An additional problem relates to bad behaviour or aggressiveness on the platform, with hurtful comments or other types of behaviour intended to disrupt the online atmosphere42.The concept of problematic use of the internet, social media and video games is extremely complex39, spending more money than expected on video games, and making hurtful comments to other people11. Therefore, potential problematic use is understood in a non-clinical sense, but it has the potential to become actual problematic use85. In this regard, we will follow the argument of Ferguson, et al.86 and we will use the term pathological gaming or use of video games, which has the advantage of being widely used and avoids the terminological concern of making a misleading comparison with substance use.In line with the aforementioned literature, this study adopts a broad definition of potential problematic use, relating to the various current behaviours that can lead to future problematic use if they persist. These include using or watching video games to forget about real-life problems, experiencing a feeling of distress when not playing, neglecting other important tasks to play or watch, spending more and more time gaming or watching video games87. Among the most influential motivational factors when gaming or watching others play video games are the perceived utility as a source of escapism and fun, social inclusion and the strengthening of friendship networks, and the need for improvement and information88. The most widely used theoretical framework for understanding the reasons that lead millions of users to watch others play is the uses and gratification theory40. This approach provides an explanation for media consumption behaviour90 based on the motivations for use and rewards that users expect to gain when they use a specific type of communication media. Four key principles underpin the theory: there is an objective and a motivation for using the medium; it is used to satisfy desires or needs; there are social and psychological factors that influence the use of the medium; and lastly, there is a relationship between interpersonal communication and the medium used91. According to these assumptions and the live streaming research that has applied this approach93, there are two main types of gratification related to the use of such platforms. On the one hand, there are the gratifications sought, relating to the instrumental motives for use and expectations about the benefit that will be attained; on the other hand, there are the gratifications obtained, which refer to the needs that are met94. Both types affect the intensity and frequency of use of such services94. Building on this framework, authors have identified as many as five motivations that determine the use of streaming platforms such as Twitch 95.Motivation is one of the aspects that has the strongest influence on the desire to keep playing96. In this regard, Khang et al.33 note that the user's motives—such as the need to escape, a way of passing the time, entertainment, feeling a sense of community, achievement or satisfaction—are significantly associated with online gaming flow and possible adverse effects derived from intensive use.Regarding pathological use, the different motivations that lead the user to play are one of the most commonly-used predictors in the assessment of disorders stemming from gaming14, Sjoblom and Hamari50 and Hilvert-Bruce et al.95 to explore the impact of motivation on the potential negative use of live streaming.In this study, we apply the main motivations identified by Hamari and Sjoblom97. This approach is based on the theory of self-determination98, which holds that the three basic psychological needs  provide greater intrinsic motivation and therefore affect the activity to be done. With reference to this framework, Mills et al.97 explored whether the perception of meeting these types of needs can become a trigger for undesired effects. Said study Mills et al.97 revealed significant and positive correlations between frustration outside the realm of video games and pathological use of video games. Consequently, it could be argued that when people perceive positive uses related to their needs being met by video games, they could seek refuge in video games to compensate for shortcomings in other areas, which in turn could cause them to neglect other tasks to the point of triggering problems associated with excessive gaming activity11.People can satisfy a wide range of needs by playing or watching video games: for example, needs related to competition, as they attain the necessary knowledge and skills to play the game well; needs related to autonomy, as they feel a sense of ownership of their decisions within the game or when live streaming; and needs related to relationships, affiliation and belonging, as they experience the feeling of being part of a larger community sharing the same interests, or build up their network of friends100. The notion of expectancy is grounded in the assumption that behaviour is influenced by personal aspirations and the reinforcing or punitive effects resulting from the behaviour. The origins of this approach lie in social learning and personality theories101. Wickwire et al.102 drew on this framework to explain inappropriate gambling behaviour in adolescents. In this regard, the use of streaming platforms is reinforced by the user's expectancies . In the long term, this could pose a problem if expectancies related to video games are prioritised over others involved in vital development103.Furthermore, gamers and viewers of video games come to live streaming platforms with certain expectancies: of developing skills and abilities, improving their relationships, learning, and even finding job opportunities and inclusion97 with that of Wickwire et al.102, defining the perception of positive use as: “the anticipation experienced by the user and expectancies linked to the possible perceived benefits of playing video games relating to three specific facets: making friends or strengthening friendships; improving skills or competencies; and the possibility of professionalizing the hobby, which in turn will lead to greater autonomy”11.In our analysis, we incorporate the approach of Mills et al.104 established the following four characteristic profiles: “achievers”, or players focused on tackling challenges; “socialisers”, focused on developing relationships, creating contacts and interacting; “explorers”, motivated by a desire to understand the details of the game and the game world; and “killers”, players predominantly driven by competitiveness. These days, the rise of e-sports15 and live streaming18 has introduced a new way to further classify gamers into e-sport gamers and recreational gamers96. E-sports gamers considers video games as serious leisure, an activity that lies somewhere between a hobby and work, and are driven by intrinsic, personal and social motivations that entail the gratification of competitive and hedonic needs96. These gamers show very high levels of affiliation (need for support and interaction with others), commitment and diversion (need for tension and exciting new experiences)96. Conversely, recreational gamers engage in their hobby in a calmer, less intense way. In this regard, in the field of esports players, Hedlund105 developed a typology based on six psychographic  and behavioural factors, identifying up to five types of players: (1) Competitive, (2) Casual, (3) Casual-Social, (4) Casual-Fun, and (5) Casual-Competitive. Similarly, Cabeza-Ramirez et al.1 categorised gamers and viewers according to their personality, playing and viewing time, skill and preferred genres, and identified four profiles: casual, social, hobby, and problematic or pathological. Nonetheless, when considering personal and time-linked characteristics, recent typologies capture the individual's commitment to their hobby, so a high time spent, or a high level of commitment to the hobby should not necessarily be associated with adverse effects74.There are different types of video game players or viewers, which have evolved over time. In the 1990s, Bartle96 showed that disruptive behaviour and psychiatric distress can appear in any type of gaming, whether intensive, recreational or professional106. Similarly, in their analysis of the dynamics of progression in online games, Wu et al.107 showed how enhanced skills and experience can cause participants to seek out new challenges in order to experience flow and other benefits. There can be a troubling downside to this, in that participants may develop pathological tendencies107. Specifically, their analysis revealed that highly specialised gamers showed significantly higher addiction rates. Moreover, it has recently been shown that recreational gamers who consider themselves experts or quasi-professionals display a worrying spectrum of pathological use and bad behaviour online1.The recent study by Banyai et al.11. Applicable under this assumption are self-determination theory98, which holds that the satisfaction of the three basic psychological needs  can explain the time spent watching live streaming, uses and gratifications theory40 and the framework of expectancies and social learning101, which provide an explanation based on the anticipated possibilities and gratifications obtained. In this vein, the matter of time spent playing video games is controversial and it has not been systematically demonstrated that intensive use can have negative connotations108. However, in different studies, relationships or effects have emerged that could link the variable with pathologies derived from the use of video games31, especially as time spent increases and time available for other activities decreases103.Playing and watching video games are two closely linked activities49. As pointed out by Buiza-Aguado49, those adolescents who have less of a perception of the negative consequences of gaming may make decisions that put their psychosocial health—and even their physical health—at risk, due to neglecting important activities such as hygiene, sleep, or eating in order to play video games. There are other important variables related to the user's sociodemographic profile that have long been observed that could be associated with pathological use; for example, the gender of the user, the type of video game32, or the time spent11. It is commonly believed that men are more intensely involved in multiplayer role-playing games, and tend to be more likely to show problems with video games109. However, society and trends are changing at a dizzying pace: there are more and more active gamers, and more cases pointing to a rise in the number of gamers treated for problems with video games110, for whom the amount of time spent or types of video game played vary widely96. Based on the above, we include both variables and propose the network summarised in Fig. Adolescence is considered a period of heightened vulnerability to experience possible overuse of video games due to a lack of maturation in some of the social and emotional regions of the brain, less cognitive control over behaviour and a propensity to seek out emotions111. The nature of ANNs lies in the development of an information processing model similar to the biological system of the human brain. The most remarkable feature of this artificial brain is the possibility of designing a structure capable of processing information by arranging key elements interconnected in the form of neurons, thus working together to provide an answer to a specific problem111. ANNs have become a useful tool for classification, pattern recognition, and prediction in different disciplines114. The multilayer perceptron approach is a specific type of ANN that is developed through simple interconnected nodes (neurons) representing a non-linear mapping between input and output vectors. It is shown graphically in Fig. From a methodological point of view, an ANN is a branch of machine learning (ML), which is an alternative to conventional regression models and other statistical modelling techniquesTwo phases can be distinguished in the implementation of this type of network: the propagation, and the second learning phase. In the propagation phase, the output results of the network are calculated from the input values forwards since this type of network is feed-forward. In the second learning phase, the errors obtained at the output of the perceptron are propagated backwards, with the aim of modifying the weights of the connections, in order to make the estimated values of the network increasingly resemble the real ones. This approximation is accomplished by means of the gradient function of the error. Therefore, ANN has the advantage of detecting both linear and non-linear relationships, and of learning through artificial intelligence functions. The rationale of ANN is to simulate the biological learning behaviour of a brain by means of small computational units, the neurons, arranged in layers connected by digital links, the synapses. The first layer captures external data , and distributes the data to the next layer via links, which are strengthened or weakened following processes defined by mathematical rules. The data is transformed as the neurons propagate the information and the top layer provides a response . This process, if repeated thousands of times, through feedback mechanisms, allows the neural network to learn the differences between patterns and detect them more accurately.46. Additionally, hedonic models have the limitation of only one variable estimation. This reason discards the use of this method as the present research considers various dependent variables (different negative uses) to be influenced by the independent ones selected. For all this, it is considered ANN as a starting point Although the most significant aspects of the proposed methodology are summarised below, further details of its advantages and disadvantages are described in Leong et al.44, as well as the main aspects of its application in Leong et al.45 and Liébana-Cabanillas et al.46.Consequent to the aim proposed, several statistical techniques are considered. For an analysis of the influence of one variable to another, correlation coefficients are commonly used. However, this method only provides information on one isolated variable compared to another. Other methodologies such as hedonic models (regressions) are common in literature, but in studies comparing these models with ANN, the latter tends to present better goodness of fits than linear regressions, due to its nonlinearity, and also a more complex arithmetical procedure than hedonic43 define an ANN as a network made up of a series of nodes—or processing elements (PE)—with a certain capacity for storing information. These PE are composed, first of all, ofan input vector  that includes the independent variables , with the corresponding synaptic weights , which are applied to the input vectors using a propagation rule (based on the corresponding linear combination). An activation function is applied to this result determining the value of these PE, grouped in layers: input layers, intermediate layers (or hidden layers), and output layers that correspond with the estimation of the dependent variables. Neural networks represent a powerful data processing technique that has reached maturity and has been extensively applied116. The main appeal of this methodology lies in its remarkable information processing characteristics, nonlinearity, high parallelism, fault and noise tolerance, and learning and generalisation capabilities112. ANNs offer a high degree of fit and have previously been applied in different fields in social sciences118 and health114, including analyses of types of problematic substance use and health problems119. Although the use of ANNs is still new in the field of dependency analysis, their capacity has recently been revealed, especially for the development of hybrid methodological models in combination with structural equations44.Rumelhart et al.R2) and mean absolute percentage error (MAPE), of the MLP type in this case. Further insight into the advantages and disadvantages of ANN implementation can be found in Gardner and Dorling115, more details on the methodological application in Basheer and Hajmeer112 and Dreyfus116, and recent developments to overcome some of their limitations in Leong et al.45, Liébana-Cabanillas et al.46 and Leong et al.44.The software used to develop the ANN is SPSS Statistics v. 23. The input values correspond to motivations, perceived positive uses, age, gender, self-perceived level as a gamer and viewing time (independent variables), while the output values are potential problematic use (dependent variable). Within ANN, several types of networks coexist, such as the multilayer perceptron (MLP) and the radial basis function (RBF). Several tests are performed taking into consideration both systems, where different topologies and activation functions are tested in the network development process, before finally selecting the ANN that shows the best fit in terms of the coefficient of determination (29. The target population for the study was Spanish people aged over 16 years old. The data were collected via the SurveyMonkey platform during the month of February 2020 (just before the Covid-19 lockdown). A link was generated and distributed on social media, video game forums, and Spanish Twitch channels via text messages encouraging recipients to distribute the questionnaire to their close contacts who were gamers and viewers of video games. The introduction included information on the approximate duration of the survey (10 min), purpose of the research, rights regarding voluntary participation and guarantees of anonymity and confidentiality. It was stated that by clicking to complete the questionnaire, respondents were giving their informed consent. Prior to distribution, the authors consulted the ethics committee of their university (Bioethics and Biosafety Committee and Research Integrity Committee of the University of Cordoba), and the principles and recommendations of the Declaration of Helsinki were observed in the research.The exploratory descriptive analysis was conducted through convenience sampling, an approach which makes it possible to access relevant information quickly and cheaply120 (p. 22). The sample is mostly men (approximately 70%) and the most representative age range is 18–25 years old, accounting for almost 50% of respondents. Approximately three out of four respondents work, and most have a mid-level or advanced educational level.The data collection started with a first draft of the questionnaire, which was reviewed by a group of five researchers to check the logical order of the questions, and the validity and reliability of the measures. A pilot test was then carried out, also through convenience sampling N = 15), and based on the results, the questions were reformulated. The answers to the pilot test were not included in the final analysis, so as not to bias the results. The final sample consisted of 970 valid responses after excluding questionnaires that did not report viewing time. Apart from this, no further restrictions were imposed on the selection criteria, in order to ensure a high degree of randomness. Table 5, and ba120. It can be seen that time spent gaming exceeds viewing time, and is not particularly high. Gamers who see themselves as having a medium level predominate (33.61%), although 30% consider themselves experts or pro. In order to play and watch, they mainly use smartphones, PCs and PlayStations, and in terms of platforms, Twitch followed by YouTube Gaming. Regarding preferred genres—each respondent was allowed to choose three options—the action and adventure genre stands out. Only 7.53% of respondents reported spending money on streaming platforms, mainly through subscriptions. Lastly, nearly 12% claimed that they live streamed or had done so in the past.Table 121, Hilvert-Bruce et al.95, and Sjoblom and Hamari50. Another section addressing positive perceptions about video games and streaming platforms based on the scales proposed by Wu et al.122 and Wickwire et al.102, adapted to measure three expectancies: making new friends, supporting education, and the possibility of turning a hobby into a profession. Lastly, pathological use was assessed in a non-clinical sense, adapting the questions from the social media addiction scale provided by Andreassen et al.39, and including two specific questions about making hurtful comments42 and spending too much money on video games123. The variables were measured on a five-point Likert scale. Table The questionnaire included three additional sections. A section dedicated to motivation, in which the measures were adapted from those previously used by Gros et al.The architecture of the network is presented in Table To produce the ANN, the sample was divided into training and test groups Table . The traR2) indicates the percentage of variability in the actual values that can be explained by the variance in the estimated values. Finally, the root mean square error (RMSE) presents the square root of the variance of the residuals, so the differences between the real and the predicted values  in absolute terms. The results  when each of the independent variables is at its minimum or maximum value, respectively, while the rest of the input values remain as mean values, or the majority in the case of nominal variables. Table For example, it can be seen that as the perception that video games have helped the respondent to create new friendships increases (PU01), there is also an increase in negative feelings caused by not being able to play (NU02), the use of video games to escape real life (NU01), or the perception that the respondent’s use of video games is increasing (NU06). These last two effects also increase as the self-perceived level as a gamer (SPL) does. On the other hand, hurtful comments to other users (NU03) increase in line with respondents' perception that the platforms contribute to their education (PU02), the feeling of belonging to the community (M501) and following tournaments and events (M103).In terms of inverse relationships, it is significant that as the age of the respondent increases, we see a decrease in all the negative uses except NU05. It is also noteworthy that an increase in respondents' interest in following specific streamers (M102) leads to a reduction in several negative uses, such as the use of video games to escape real life (NU01), making hurtful comments (NU03), negative feelings when unable to play (NU02) and the perception of the growing use of video games (NU06).37. The exact number of users affected by IGD is currently unknown. The recognition of pathological gaming as a health problem is even questioned124, and misdiagnosis can occur due to a high commitment to the hobby74. One of the largest meta-analyses carried out suggests global prevalence rates of around 3%. Nevertheless, there is significant variability in these estimates due to a higher proportion of analyses being carried out with samples of adolescents, or the use of different measurement scales125. Another important aspect concerns the influence of the co-occurrence of linked activities on potentially problematic use. This has been shown by Reer et al.37, Sussman et al.38, and Andreassen et al.39, who demonstrate feedback between and coexistence of adverse forms of behaviour stemming from the combined use of the internet, social networks and video games. Our results add to the emerging research in the field of live streaming and align with recent studies about possible adverse effects31. We can highlight the following findings:The increasing digitalisation of everyday life offers obvious benefits, but could have a dark side39, specifically, playing or watching to forget real-life problems (NU01), feeling bad when unable to play or watch (NU02), and spending more and more time playing or watching video games (NU06). This result does not seem surprising given that platforms like Twitch are fundamentally social by nature127. In this regard, Hilvert-Bruce et al.95 note that the amount of time viewers dedicate to live streams is significantly explained by social interaction and a lack of external support. This finding is consistent with those reported by Mills et al.97, who showed significant and positive correlations between frustration outside the sphere of video games and pathological use of video games. From a practical standpoint, the results appear to confirm that social support from parents, family and friends could be an extremely useful tool for preventing possible disorders derived from video games28.First, we found that the social component linked to the positive perception of making new friends (PU01) is related to three variables that are important when it comes to a clinical assessment of the pathological use102 claimed, if the player feels that he/she is making progress when using the live stream, and is continually improving, he/she may prioritise this type of activity to the point where it becomes excessive. In practice, actions could be taken to raise awareness among gamers or viewers, targeted by ability groups; a casual novice gamer is unlikely to have the same expectancies and thus experience the same problems as someone who has a higher level, especially if that gamer feels that he/she is making ever greater progress.Second, we found that self-perceived level as a gamer (SPL) is a variable worth accounting for in the assessment of pathological use. Specifically, we observe that it is related to the following variables: spending more and more time watching or playing (NU06) and playing or watching video games to forget about real-life problems (NU01). In this respect, users' gaming skills and the progress they make as they play (at least as they perceive it) are rarely included in analyses of disruptive behaviour. The relationships identified are in line with the theory of social learning and expectancies; that is, as Wickwire et al.42. Hilvert-Bruce and Neill41 claimed that harassment is viewed as normal and is even tolerated in these contexts. Similarly, Lee et al.128 reported that verbal attacks are common in multiplayer online games. Their results suggest that there is a certain propensity to make hurtful comments in the competitive sphere—for example, in tournaments and events—when the user feels that he/she is learning to improve and forms part of a larger group involved in a large League of Legends (LoL) tournament. From a practical point of view, this suggests that organisers of events and platforms should introduce measures aimed at raising public awareness to maintain a good gaming environment and prevent any type of aggression, especially in environments of strong commitment to video games.Furthermore, the motivational variables related to being part of a community (M501), following tournaments and events (M103), and even the positive use linked to the influence of video game platforms on respondents' education  are related to the probability of making hurtful comments to other users (NU03). In this respect, there have been a fair few studies that have warned of the toxicity of the comments and the adverse environment generated in certain online communities11, users who perceive positive effects of playing video games might be more likely to develop negative behaviours through the time they spend playing and less through watching, as they prioritise the first activity. This finding thus points to a need for further empirical research on the co-occurrence of watching and playing.Fourth, it is striking to note that the variable viewing time (WGV) does not seem to be related to pathological effects; that is, negative potentialities do not emerge as viewing time increases. This may be due to the fact that the variable time spent gaming was not included in the neural network, since the intention was to explore the negative potentialities stemming from viewing. Therefore, future research should explore the cumulative effects of both times (gaming and viewing) on the potential negative effects of the use of video games. As noted by Cabeza-Ramírez et al.129, and when video games fulfil certain expectancies and needs that are not fully satisfied outside of this sphere130. The case of age was especially notable as it influences a number of variables: spending more and more time playing/watching video games (NU06), neglecting important tasks (NU04), feeling bad when unable to play or watch (NU02), making hurtful comments to other users (NU03) and playing or watching video games to forget real-life problems (NU01). To an extent, this underscores the belief that some pathologies caused by video games in people's lives can be contextual, and they tend to decrease as cognitive control over behaviour increases49. In addition, longitudinal research on IGD in studies carried out with samples of adolescents has shown the emergence of transient gaming disorders, characterised by self-recovery131.Lastly, the variables following specific streamers (M102) and age—especially the latter—were found to reduce pathological effects. This indicates that instrumental use of the platform does not in itself generate negative potentialities, but it does when accompanied by certain aspects of the user's personality40. The study makes three novel contributions relative to previous studies. First, it includes variables related to the user's gamer profile, self-perception or experience as a player, and viewing time18. Second, it accounts for the positive expectancies of the video game player-viewer as possible triggers of pathological use98. Finally, the methodology applied, neural networks, is a powerful data processing technique116, whose main benefits include nonlinearity, high parallelism, fault and noise tolerance, as well as learning and generalisation capabilities112. This type of analysis is starting to be used to optimise decision-making based on cohort studies of substance use disorders119. In the near future, it seems likely that ANN studies in the field of live streaming research will have a place in detecting patterns of live streaming consumption and usage. Recent research is adopting a two-step hybrid approach, using structural equation modelling to establish relationships and directions of causality, and neural networks to optimise prediction and pattern detection, as for example Lo et al.132 apply this hybrid approach to the analysis of the motives underlying impulse buying during live streaming. However, the choice of approach depends on the complexity and degree of theoretical understanding of the problem. In this case, as has been highlighted throughout the paper, there is an open debate about pathological gaming as a mental health problem. The set of variables that are most significant in assessing the differences between engaged and pathological use are also not precisely known74. The multilayer perceptron technique, unlike other statistical techniques, does not make prior assumptions about the distribution of data, can model non-linear functions and can be trained to generalise accurately if new data become available115, making it a valid alternative for shedding light on the understanding of the variables involved in such a complex phenomenon115.The empirical analyses carried out in this paper add to the emerging research on public health, video games, live streaming and disruptive behaviour111. Additionally, the proposed neural network can be expanded to include other variables such as time spent gaming, preferred genre, and variables related to the user. In this regard, the cross-sectional approach used limits the scope of the research. Furthermore, the observed relationships between network variables could be caused by the influence of other factors that have not been considered in the research, requiring some caution in interpretation. This implies the need to include in future research additional statistical controls with the inclusion of control variables , that is, part or all the observed relationships could be explained by other variables. Therefore, future research should consider the use of neural networks combined with structural equation modelling, which would allow determining causal relationships as well as increasing the reliability and validity of the results46. Second, the concept of pathological use was applied in a non-clinical sense, pointing to future experimental designs in which the effect of the variables included here can be compared between people who have been clinically diagnosed and those who do not suffer any problems stemming from the use of video games. According to Gardner and Dorling115, one of the limitations of this methodology lies in the decisions associated with the construction of the network, i.e. the number of layers and nodes. There are no specific rules in this respect, the inputs and outputs are determined by the problem in question. The optimal number of nodes in the hidden layers is related to the complexity of the input–output mapping, the noise of the data and the number of cases available for training. Future research should focus on constructing a network to detect patterns of real or clinical pathological use. This will require feeding the network with a training dataset divided into several sets, including a validation and a test set. The validation dataset should be composed of data from actual diagnosed users. Third, this cross-sectional quantitative research was conducted using a relatively large convenience sample; that is, the findings might not be generalisable, even though the profile of the sample resembles that reported by AEVI120. As such, future studies should apply stricter sampling criteria; for example, stratified random sampling. Furthermore, there is still limited research on the convergence between the active use of video games and watching11.The present research is not without its limitations, though some of them represent interesting opportunities for future research. First, this is an initial, exploratory approach with a novel methodology (in this field of research); accordingly, it is limited to a set of variables that could be determinants of pathological use. There is thus a need to confirm the results using other approaches such as data mining, regression models and other statistical analyses49.Lastly, this is a static study that captures possible variables at a given time, which opens the door to new longitudinal analyses that address the evolution of the user as a player and viewer of video games


LFI and its subtest chair stands had the best discriminative ability to predict rehospitalization, with AUROCs of 0.66 and 0.67, respectively. An LFI cut-off of >4.62 discriminated best between patients with and without elevated risk for rehospitalization within 30 days. Conclusions: Measures of frailty could be useful to identify patients at higher risk for short-term rehospitalization.Background: Stratifying patients with liver cirrhosis for risk of rehospitalization is challenging with established scoring systems for chronic liver disease. Frailty captures the physical characteristics of patients with cirrhosis. Its value for predicting short-term rehospitalizations in hospitalized patients remains to be defined. Methods: Eighty-three non-electively hospitalized patients with liver cirrhosis were analyzed in this study. Frailty was assessed during the last 48 h of hospital stay with the liver frailty index (LFI). Patients were followed for 30-day rehospitalization. Results: In total, 26 (31%) patients were rehospitalized within 30 days. The median LFI was 4.5, and 43 (52%) patients were identified as frail. Rehospitalized patients had a significant higher LFI compared to patients without a rehospitalization within 30 days. In multivariable analysis, LFI as a metric variable (OR 2.36,  Frailty represents a clinical syndrome complex including a decline in physical and physiological reserves, ultimately leading to vulnerability to adverse health outcomes ,2. In thLiver cirrhosis is the end-stage of almost every chronic liver disease. It is characterized by an excessive accumulation of scare tissue, which disrupts the liver’s delicate cellular architecture . PatientIn recent years, the liver frailty index (LFI) has emerged as a valid and easy-to-use tool to assess frailty in patients with liver cirrhosis. LFI is based on three objective physical performance tests, including hand-grip strength, chair stands, and balance exercises, and has been shown to improve risk prediction for mortality in cirrhotic patients awaiting liver transplantation . In addiThis study aimed to evaluate the usefulness of the LFI for predicting short-term rehospitalization of hospitalized patients with liver cirrhosis.In total, 88 cirrhotic inpatients were prospectively recruited, and data were included into a database between September 2019 and December 2020 at the Cirrhosis Center Mainz of the University Medical Centre of the Johannes Gutenberg–University in Mainz (Germany). For this post hoc analysis (retrospective), five patients were excluded due to active malignancies or loss to follow-up . FinallyPatient characteristics were recorded during the last 48 h of each hospital stay. Etiology of underlying liver disease was determined according to clinical, serological and histological findings together with anamnesis. Cirrhosis was diagnosed by an experienced hepatologist considering histology, typical appearance in ultrasound or radiological imaging, endoscopic features of portal hypertension, and medical history. History of decompensation and blood biochemistry were recorded in detail for each patient. MELD and Child-Pugh (CP) score were calculated to determine the severity of liver disease ,11.Hand grip strength: the average of three trials, measured on the patient’s dominant hand using a hand dynamometer.Chair stands: measured as the number of seconds the patient needs to perform five chair stands with arms folded across the chest.Balance testing: measured as the number of seconds the patient manages to balance in three positions  for a maximum time of 10 s each.After recompensation, each patient was tested with the LFI as part of routine clinical practice during the last 48 h of their respective hospital stay. The LFI consists of the following three physical tests and was administered by a trained healthcare professional: http://liverfrailtyindex.ucsf.edu (accessed during the study period September 2019–December 2020). Higher LFI values indicate a higher degree of physical functional impairment. Patients with an LFI value of ≥4.5 were considered frail [LFI was calculated based on the results of the administered tests, applying the online available calculator at The study was conducted according to the ethical guidelines of the 1975 Declaration of Helsinki and its later amendments. The study was approved by the ethics committee of the Landesärztekammer Rhineland-Palatinate. Written informed consent was obtained from each participant.Data were analyzed using IBM SPSS Statistic Version 27.0  and GraphPad Prism Version 8.0.2 .t test or with the Mann–Whitney U Test. Categorical variables are expressed as frequencies and percentages. For comparison of two or more patient groups, a chi-square test was applied. Correlation analyses were conducted using Spearman’s rank correlation.Quantitative data are expressed as medians with interquartile ranges (IQR), and pairwise comparisons for quantitative variables were performed with an unpaired To reliably identify factors being associated with 30-day rehospitalization, we conducted multivariable logistic regression models based on a stepwise variable selection procedure. To investigate the discriminate ability of the respective regression models, Harrell’s C-index was calculated. Additionally, we also conducted time-to-event analyses using Kaplan–Meier curves and Cox-regression analysis based on a stepwise variable selection procedure.To investigate how the LFI and its subtests discriminate between patients with and without rehospitalization within 30 days, we calculated area under the curve of receiver operating characteristic (AUROC) curves and their respective 95% confidence intervals (95% CI). Thresholds for the LFI and its subtests were determined based on the Youden’s index .p values should be interpreted with caution. Our complete data analysis is exploratory. Hence, no adjustments for multiple testing were performed. For all tests, we used a 0.05 level to define statistically relevant deviations from the respective null hypothesis. However, due to the large number of tests, In total, data of 83 inpatients with liver cirrhosis were analyzed in this study. Sixty percent of the patients were male with a median age of 60 years . The main etiology for liver cirrhosis was chronic alcohol consumption (66%). The median MELD score at study inclusion was 17 , while 31% were categorized in Child-Pugh class C. In the total cohort, the median LFI was 4.5 , which corresponded to 52% of the patients being classified as frail. The majority of patients had a history of ascites (83%), and 35% had a history of HE. Additional baseline characteristics of the entire cohort are displayed in In total, 26 (31%) patients were rehospitalized within 30 days, and no patient died without being rehospitalized. Volume overload was the most frequent (75%) diagnosis leading to rehospitalization. Rehospitalized patients differed significantly from patients without rehospitalization, for instance in terms of MELD score, history of ascites and LFI. The comparisons between both groups are displayed in Data are expressed as median with interquartile ranges (IQR) or as frequencies with percentages. HE, hepatic encephalopathy; LFI, liver frailty index; NAFLD, non-alcoholic fatty liver disease; SBP, spontaneous bacterial peritonitis; WBC, white blood cell count.p < 0.01) and higher LFI  were independently associated with rehospitalization within 30 days (p = 0.09 for MELDNa score).A multivariable logistic regression model with a stepwise variable selection process was applied to assess risk factors for non-elective rehospitalization within 30 days. In a model including LFI as a metric variable, lower platelet counts  and higher LFI  were independently associated with time to rehospitalization within 30 days . We conducted ROC analyses to assess the performance of the LFI and each of its subtests to predict rehospitalization within 30 days . AUROCs Predicting rehospitalization of patients with liver cirrhosis is challenging in clinical practice. In this study, we found that higher LFI scores—a measure of frailty—were independently associated with a higher risk of 30-day rehospitalization in hospitalized patients with liver cirrhosis. Additionally, we demonstrated that the diagnostic accuracy to predict rehospitalization was comparable between LFI and its subtest chair stands, although the discriminative ability of these measures was only mediocre. These findings expand the growing body of evidence indicating the usefulness of frailty assessments for prediction of clinical outcomes in patients with liver cirrhosis.Frailty is common in patients with liver cirrhosis and is associated with a higher mortality in outpatients irrespective of other cirrhosis-related complications . StudiesIt has to be acknowledged, however, that the discriminative ability of the LFI was only mediocre (AUC 0.66) in our study and is far from the results required for a reliable stand-alone test. Additionally, we only found an association between LFI as a metric variable and not frailty according to a cut-off of >4.5 in logistic regression analyses besides platelets and serum sodium. These findings emphasize that the risk of rehospitalization seems to be mainly determined by portal hypertension (as reflected by lower platelets) and poorer physical function (as reflected by LFI). In this context, the established cut-offs for frailty in cirrhotic outpatients (LFI > 4.5) may not apply for the prediction of rehospitalization. The ideal cut-off in our cohort was slightly higher at 4.62 according to the Youden’s index. However, our findings have to be interpreted in the context of the study design, and more reliable cut-offs should be developed in future larger multi-center studies.Our current findings have several clinical implications. Using the LFI before discharge of patients with liver cirrhosis may help identify those in need for intensified support and interventions. There are several targets for improvement of frailty such as home-based physical therapy, pharmacologic therapies, as well as adequate pain management . AdditioSeveral contextual limitations to our study have to be acknowledged. First, our study is based on data from a single tertiary care center, which may affect generalizability. Additionally, our sample size is only mediocre, and the results should be interpreted as a proof-of-concept. Therefore, larger multicenter studies are needed to establish robust cut-offs for predicting rehospitalization and to clarify whether the chair stands subtest is sufficient to predict rehospitalization or whether the full LFI is required. Second, due to our study design, we were only able to identify potential associations between different variables and short-term rehospitalization, and causality has to be proven in future studies. Last, we assessed the LFI only once during the last 48 h of each hospital stay according to clinical routine. Therefore, we are unable to determine the ideal timepoint for testing patients with the LFI to predict rehospitalization. Future studies should focus on longitudinal assessment of LFI during hospital stays of patients with liver cirrhosis.In conclusion, our study adds to the growing body of evidence indicating the usefulness of the LFI for predicting clinical outcomes in patients with liver cirrhosis. We found that poorer results in the LFI were independently associated with a higher risk of 30-day rehospitalization in patients with liver cirrhosis, although the discriminative ability of the LFI was only mediocre. Nonetheless, our results warrant further research on this topic to validate our findings in larger cohorts and to establish robust cutoff values.


This qualitative case study examined how a multimodal professional network environment  affected five STEM educational researchers’ capacity to engage in grant funded research at U.S. Historically Black Colleges and Universities (HBCUs). Guided by the social capital and professional network literature as a conceptual framework, we analyzed data from surveys, interviews, and online discussion posts. We aimed to understand HBCU-based researchers’ supports and barriers in writing and/or conducting grant funded research in STEM education, and ways in which the multimodal professional network experience supported their research and professional networking, if at all. We found that organizational structures shaped participants’ social capital as well as their grant funded research activities. Further, participating in a multimodal professional network enabled participants to further develop their research capacity and to also expand their collegial networks. We offer recommendations for institutions to support the research endeavors of their faculty and suggest ways in which organizations using or developing professional multimodal networks can enhance faculty research development. The imperative to mount a significant research program as a way to enhance prestige has increasingly affected institutions of higher education whose historical mandate has not solely focused on scholarly productivity  is an annual eight-day interactive event where over 1,000 researchers create, share, and discuss three-minute videos about their federally funded programs. Videos presented in 2021 have been viewed (to date) over 50,000 times by over 100,000 unique visitors across the globe. During the one-week event over 7,000 discussion posts were exchanged, as facilitators, researchers, educators, and other stakeholders offered comments, resources, and feedback to presenters. Refer to Falk et al.  and the related STEM for All Multiplex site. The Video Showcase  aggregates presentations from the Video Showcase over multiple years and now houses over 1,600 easily searchable, short videos of federally funded work to improve STEM in pre-K through graduate education. It also engages the community of researchers throughout the year by providing month-long themes, drawn from the vast collection of video presentations. Each theme includes an interactive webinar, a curated playlist of videos, online discussions, resources, a blog, and a synthesis brief.The STEM for All Multiplex site  due to it being a primarily undergraduate teaching university. As Dr. Harris said:Despite the lack of structural supports, she often found supports for her research in other ways, such as through her network of internal colleagues. Dr. Harris relied on her department chair for budget questions, and she felt she was supported by “the close-knit” relationships with colleagues in her department, from whom she could seek advice. However, as with all other participants in this study, there was no formal mentoring program at her institution.Institutional supports and barriers  shaped researchers’ social capital and grant funded capacity within the field of higher education, with its push for prestige connected to research and call for multi-institution collaborations. Like Dr. Harris, other participants felt their institution valued research, yet found few structural supports to advance their research capacity. Furthermore, all participants mentioned individual people within their institution that they relied upon for advice or support, rather than institutionalized supports that could expand their network beyond their institution and thus their social capital. Other participants mentioned a desire for a “formal mentorship program,” “more infrastructure,” and “clear connections with faculty networks” beyond their institution to avoid “isolation.” Therefore, their organizational habitus, shaped by the field, limited their social capital and restricted their capacity to grow as researchers.Engaging in the Video Showcase offered participants the opportunity to further develop their research in four interrelated ways: (a) encouraging reflection, (b) developing a product, (c) becoming aware of related work, and (d) receiving feedback. In the code visualization Fig. , this fiIn preparation for the Video Showcase, participants had to create a 3-min video highlighting their research. The Video Showcase staff provided participants with online webinars on how to create storylines for an effective video. This was a reflective process that offered participants insight into refining their work as they prepared to present it to a general audience. All five participants discussed the opportunity to reflect on their work through their participation in the Video Showcase.We spent a lot of time just working together, really figuring out what we want to do. And all of our voices were heard, which I loved… . That really did help us reflect on what we were trying to do and make sure that we get it accomplished.For example, Imani Silva was program coordinator for an NSF INCLUDES grant at a public HBCU in the Southeastern United States. She was an emerging scholar and was simultaneously pursuing her master’s in a social science field. The members of her team all took active part in making the video, and she thought the process helped them reflect on their progress and future goals. She said:In this way, the making of the video provided the participants with an opportunity to reflect on their work and come together as a team.After making the video, participants had a product they could use to further disseminate their work and advance future research. Benefits of the video, aside from the presentation itself, included using it as a product in grant renewals, posting it on program websites, and learning video skills to use for future projects. Four of the five researchers discussed ways in which they used the video they developed from their participation in the Video Showcase.To highlight this point, we offer the case of Deja Jones, a visiting assistant professor and director at a public HBCU in the Southeastern United States. She presented two videos in the 2021 Video Showcase, one on an NSF Established Program to Stimulate Competitive Research (EPSCoR) grant, and the other was for her work as a co-director on an NSF INCLUDES grant. She posted the video on the “larger grant website” and appreciated that it made the site more “interactive” than a typical grant site. Dr. Jones was encouraging her team to create additional videos for “different layers of constituents” because she realized what a “valuable resource” the video medium could be to communicate to different audiences. Further, she was using the Showcase video for “courting partners and supporters” because it was a medium that provided a “good synopsis” of her work without asking people to “read through scores of [written] material.” Therefore, Dr. Jones used the video to disseminate her work to various audiences and advance future, related research.The Video Showcase platform also helped all participants become aware of related work, which increased their knowledge related to their research topic. When one is viewing a video, the Showcase webpage presents a list of six related videos, making it easy for presenters to find videos that are similar to theirs, even if funded by different programs within NSF or by a different federal agency. In addition, the Video Showcase allows participants to search all presentations by keyword, age/grade level, funding agency, institutional type, or state to find those of interests. Participants are also able to easily view the videos of researchers who comment on their video through a signature link left in the comment.An example of the benefit of becoming aware of related work is from the aforementioned case of Ms. Silva. During the Showcase, she used the keyword filter to try to “see what else was out there” related to her video topic on workforce interventions. She became “pretty excited” in “seeing the programs that were a bit similar” to her own. Her goal in watching other researchers’ videos was to “see the variety of what’s out there and [what] other people are doing … how we can try and use that.” The multimodal nature of the Showcase afforded her the opportunity to explore similar programs through the video medium. Becoming aware of others who are doing similar work was important for Ms. Silva, as an important part of her hub program was to build relationships with nearby workforce programs. As an early career scholar, she was also looking to expand her professional presence and build connections.The online discussion forum appended to each video on the Showcase offered feedback to the presenters. All participants mentioned some type of feedback from their video’s discussion, whether it was critiques of their work, ideas, or resources. This feedback helped researchers reflect on their work moving forward, as well as build contacts for future work as discussed in the next section.For example, Dr. Jones, previously discussed, was an established scholar and had received other research grants from multiple federal agencies. She entered the Video Showcase hoping to address specific challenges she was facing in her program, as evidenced by her video’s introductory discussion post asking for “some feedback on the following: [the intervention framework’s] transferability to higher education [and] suggestions for sustaining faculty support and training beyond the grant.” Dr. Jones noted their video received quality feedback, where “people brought a different type of theoretical model, that may be an additional explanation of what we were seeing, which was good.” This feedback on their video, which also increased their awareness of related work, sparked a conversation between the research team and one commenter. The subsequent conversations helped Dr. Jones “flesh out” and “refine” their theoretical model, which they are now using for “another grant proposal.” Dr. Jones and her team “go back to those videos’ [discussion forums] now to look at the input … to have some continued conversation and development in the team” as they refine their work. Therefore, as the online discussion forums connected to each video are archived, they serve as a stable resource to return to and to explore theories and ideas to further inform their work.The Video Showcase provided participants an opportunity to expand their professional network as well. We have conceptualized an enlarged network as comprising two categories: (a) increased professional presence and dissemination of work, and (b) increased number of connections with potential future collaborators. All participants found the Video Showcase offered them a way to disseminate their work and enhance their professional presence, and increase connections in their network. Four of the five discussed connecting with potential future collaborators. In the code visualization Fig. , this fiWhat [my mentor] eventually taught me was this is about a claim. This is about you getting your work out there to the masses. At first, I didn’t look at it from that lens. I looked at it from the point of view of I’m helping these students [engaged in the research program], wow. This is a great thing. I didn’t look at it as far as, no, you’re not only helping these students in this small pocket of the world, you eventually can touch just about everybody. So, that was very eye-opening as well.To illustrate this finding, we draw on the case of Tiana Williams. Ms. Williams was an administrator and adjunct professor at a private HBCU in the Southeastern United States, and the lead presenter of a 2021 Video Showcase presentation on her NSF HBCU Undergraduate Program (HBCU-UP) grant. As an early career scholar and doctoral student, she desired to disseminate her work to inform practice. At the encouragement of her mentor, she participated in the Video Showcase. Afterwards, her mentor showed her the activity map which the Showcase provides for each presenter, displaying viewers’ geolocations. She realized that people as far as “Hong Kong” had watched her video. While they were both “excited” that she was expanding her professional presence, it was also about increasing the reach of her project. Ms. Williams said:Like Ms. Williams, other participants used the Video Showcase to disseminate their work by promoting the link to their presentation through email and social media, or by embedding their video on other websites.Margaret Novak, an assistant professor of biology and environmental science at a private HBCU in the Southeastern United States, provides an example of finding increased connections and potential future collaborators through the Showcase. She was the lead presenter of a 2020 Video Showcase presentation on an NSF HBCU-UP grant, and a co-presenter of a 2021 presentation on a Division of Ocean Sciences NSF grant. During the 2020 Showcase, she had a higher-than-average number of posts on her video (36) and she viewed and commented on nine other videos, engaging other researchers with related work. After her 2020 video, she was invited to participate in a STEM for All Multiplex theme of the month webinar panel  where her research was highlighted. Her participation on that panel increased her professional presence and attracted the attention of another research group at a different university. She said, “they had seen the video that I made [for the Video Showcase] and then saw me in the [Multiplex] webinar” and “reached out” to “collaborate” on a grant. This project resulted in her second Video Showcase presentation in 2021. Dr. Novak felt there were multiple instances in both Video Showcase discussions where “there was a lot of potential for more collaborations.” As other study participants commented, some of the connections made during the Showcase resulted in collaborative work, and some remained at the connection level but still expanded their professional network.Results indicate that prior to the Video Showcase, organizational habitus and field shaped participants’ social capital and grant funded experiences. The Showcase offered participants an additional opportunity to develop their research and expand their network in tangible ways. These findings add to the literature by indicating how a cross funded program, cross university, free online platform could help researchers whose organizational habitus limits their research capacity build their networks and social capital. We situate these findings in the literature, and offer recommendations for practice, policy, and future research.Participants said that their institutional cultures valued research, but that they lacked the systems and structures to support or advance their capacity for grant funded research. This finding aligns with the literature on supports and barriers at HBCUs and other undergraduate teaching institutions Ellis, . InstituBased on this finding, HBCUs and other teaching institutions can identify ways to institutionalize support for grant funded research in ways that do not cost additional resources. For example, HBCU sponsored programs staff could partner with other institutions or federal programs to offer workshops and resources beyond the introductory level professional development typically offered Toldson, . InstituWe found that the Video Showcase provided participants with a valuable opportunity to develop their research through reflection, to increase their knowledge of related work, and to meet other researchers in their field and receive feedback from them. While a multimodal professional network like the Video Showcase does not have the ability to change the field of higher education, alter the organizational habitus of HBCUs, or create more equity between institutional types, it is an intervention that increases the capacity of individual researchers for grant funded research. The advantage of this intervention is accelerated through the multimodal nature of the environment, which includes video sharing, collegial discourse, and creating a feedback loop for developing research. For example, participants noted the value of the reflection process in creating a video, which was then used to disseminate their work and to further establish their professional identity. In turn their video presentation and related discussion elicited feedback from others which contributed to improvement of current work, as well as finding potential collaborators for future work—expanding their social capital. The fact that this happened asynchronously online, gave presenters both a developed product that could be reused and archived feedback for later retrieval (Falk et al., There is little research on feedback and reflection as part of professional conferences broadly, and multimodal professional network environments specifically. Some scholars have examined the nature of poster presentation modality, including how design fosters reflection and promotes knowledge transfer (Ilic & Rowe, An implication of this finding is the need for further exploration of the benefits of multimodal professional network environments for researchers’ professional development. As more journals move from print to online and offer multimodal content, and conferences move toward hybrid and online formats due to COVID-19, we need to further understand the influence of these innovations on researchers and their work. This trend will only likely increase as it offers accessibility and engagement beyond a place-based and synchronous format, opening this intervention to international researchers and audiences. Further, as federal agencies prioritize broadening participation in STEM, they have begun to encourage researchers to diversify their scientific communication to reach broader, nonacademic audiences (Farmer Cox et al., The Video Showcase provided study participants with an opportunity to expand their network in tangible ways, which enhanced their social capital. Research draws a positive relationship between collaboration and scholarly productivity (Baldwin & Austin,  A myriad of research findings points to the importance of mentorship as related to collaboration and scholarly productivity, especially for Black faculty (e.g., Ellis, First, all study participants noted that their institutions had no formal mentoring program. Formal mentoring programs often provide structure and guidance for early career scholars that is needed for scholarly success and productivity, particularly for Black women faculty Fields, . MoreoveSecond, tenure and promotion policies could be adjusted to facilitate collaboration. Baldwin and Austin  found a Finally, HBCUs could encourage researchers to develop external relationships and networks. For example, Toldson  found thThe aim of interpretive qualitative research is not to generalize, but rather through analysis arrive at “concrete universals”—that is, findings that could be similar across cases (Erickson, In this study, we set out to examine how a multimodal professional network created to allow faculty to share and discuss their research, affected HBCU-based researchers’ capacity to engage in grant funded research. We found the organizational habitus of HBCUs and the field of federal funding in higher education shaped participants’ social capital and grant funded research experiences prior to participating in the Video Showcase. The HBCU-based researchers found that participating in the Video Showcase provided an opportunity to further develop their research and expand their professional network in tangible ways. These findings illustrate how this multimodal professional network environment increased the dissemination of researchers’ work, enhanced their professional presence in the field, enabled them to receive ideas, feedback, and resources that would further their current research efforts, and finally, make connections for future collaborations on future grant proposals.While we noted that multimodal professional networks like the Video Showcase would not necessarily change the field of higher education, the creation of more multimodal professional networking environments aimed at fostering and enabling collaboration, could help bridge the gap of inequity between organizational types. Federal agencies who award these grants could consider funding more multimodal professional networks, such as the NSF has done for the STEM for All Video Showcase. These networks build capacity across agencies and institutional types, enhance research currently funded, and propel new collaborative efforts in the future. Many agencies are interesting in funding HBCU-based researchers yet struggle to do so. Therefore, multimodal professional networks are a powerful way to enhance HBCU-based researcher capacity to apply for and receive federal funding.


Scientific reports 10.1038/s41598-022-18025-x, published online 26 August 2022Correction to: The Supplementary Video S1 and Supplementary Video S2 files published with this Article contained errors, where some of the descriptive labels of the video were omitted.These errors have now been corrected in the Supplementary Video S1 and Supplementary Video S2 files that accompany the original Article.


In recent years, with the development of the internet, video has become more and more widely used in life. Adding harmonious music to a video is gradually becoming an artistic task. However, artificially adding music takes a lot of time and effort, so we propose a method to recommend background music for videos. The emotional message of music is rarely taken into account in current work, but it is crucial for video music retrieval. To achieve this, we design two paths to process content information and emotional information between modals. Based on the characteristics of video and music, we design various feature extraction schemes and common representation spaces. In the content path, the pre-trained network is used as the feature extraction network. As these features contain some redundant information, we use an encoder–decoder structure for dimensionality reduction. Where encoder weights are shared to obtain content sharing features for video and music. In the emotion path, an emotion key frames scheme was used for video and a channel attention mechanism was used for music in order to obtain the emotion information effectively. We also added emotion distinguish loss to guarantee that the network acquires the emotion information effectively. More importantly, we propose a way to combine content information with emotional information. That is, content features are first stitched together with sentiment features and then passed through a fused shared space structured as an MLP to obtain more effective fused shared features. In addition, a polarity penalty factor has been added to the classical metric loss function to make it more suitable for this task. Experiments show that this dual path video music retrieval network can effectively merge information. Compared with existing methods, the retrieval task evaluation index increases Recall@1 by 3.94. Video, a form of data that records life and conveys opinions, is produced and uploaded to the Internet by many users. Video is a media message that contains visual and auditory information. Among them, visual information is mainly presented in the form of moving images, while auditory information includes voice, ambient sound, background music, etc. Music, as an art form to express emotions, is often used to enhance the artistic effect of the video, resonate with the user and bring a better experience to the user. However, manually selecting the right music for a video is an extremely lengthy and laborious task. Short video applications often recommend music based on its popularity, and only part of the music conforms to the video. Therefore, the automatic retrieval of suitable music clips based on video information is a problem worth investigating. The study of this problem is equally beneficial in helping us to understand how art communicates between different modalities. Matching music with a given video belongs to the retrieval task, that is, the corresponding music information is retrieved through the video information. Some studies ,2,3 sugg(1)A video-music retrieval dataset has been constructed and will be published on the website. The dataset is derived from films with a certain level of popularity, and each video–music pair includes an emotional descriptor and polarity labels.(2)A dual path video–music retrieval network combining content information and emotional information is designed. It can effectively learn various information and use this information to perform retrieval tasks.(3)A more task-consistent metric loss function was designed and used. By adding penalty factors to the data pairs, the metric loss function is optimized differently for different data pairs, achieving a dynamic optimization of the objective.In this paper, we combine emotional information and content information to design a dual path cross-modal retrieval network. In the content path, a content common space with a fine network structure is designed and additional constraints are added to obtain the content sharing representation of video and music. In the emotional path, the emotional feature extraction scheme is designed to obtain emotional features. The representation of the emotional sharing of video and music is achieved by designing a network structure with simplified emotional common space. Combined with content and emotion, the common fusion space is designed, and the final representation features of video and music are achieved. By computing the similarity between features, the video music retrieval task is finished. Our main contributions are as follows:In According to video retrieval, music belongs to the cross-modal retrieval technology, where cross-modal retrieval is able to retrieve information from one modality to the other modality associated with it. However, different modal data present heterogeneous low-level features, and only matching different modal data present semantically related high-level features. Faced with such a problem, cross-modal retrieval techniques often work by learning a common/shared representation space in which the semantic similarity of different modal data can be measured. In video music retrieval, the main current research approaches can be divided into content-based and emotion-based video-music retrieval.Content-based video-music retrieval considers that both modalities have semantic content-related information, such as the consistency of the events of the video and the theme of the music, etc. Consequently, they tend to be self-supervised methods, using paired video and music pairs without additional label information. Hong S et al.  proposedEmotion-based video–music retrieval considers that music is often used as media information to express emotions, so the emotional information of music is relatively rich. Aiming for that characteristic of music, research often uses emotional information to restrict the common representation space. Shin K et al.  used linAs two different modalities, video and music have a large heterogeneity of data. Video often contains a mix of content information such as actions, objects and scenes. In contrast, music, as a kind of audio with lyrical characteristics, contains more emotional information. Based on this heterogeneity, it is effective to take emotional information into account in video–music retrieval. There is a broad base of research on emotion information for video and music, namely video emotion recognition and music emotion recognition. Video emotion recognition (VER), the main research method for understanding video content, is not limited to facial information in emotion recognition, but focuses more on the emotional information conveyed by the video . MusicalIn video emotion recognition, the effective selection of emotion frames  . The overall structure of our proposed DPVM is shown in Video music retrieval differs from other cross-modal retrieval techniques in that it does not use semantic content information as the only bridge to cross-modal retrieval. The reason for this significant difference is mainly due to the specificity of music, which tends to express lyrical emotions as its main content. This difference leads to a different technical route for video–music retrieval tasks than for other cross-modal retrieval tasks. In the image–text retrieval task , the keyIn current content-based video–music retrieval tasks, generic networks pre-trained on large datasets are often used to extract video and music features, and thus the features contain a large amount of redundant information, which leads to a misalignment of video and music feature information. For example, video features often contain a mishmash of content information such as actions, objects and scenes. Music, on the other hand, often contains information that is not as clear as video, and as a kind of audio with lyrical features, in addition to more specific content information, music also contains richer emotional information. Current emotion-based video-music retrieval tasks often use generic networks pre-trained on emotion datasets as a way to make the network capable of learning emotion representations. This approach does not effectively learn emotion information and still contains a large amount of redundant information.Based on the above theoretical foundation and the current state of research, we use a combination of content information and emotion information to solve the video–music retrieval task, and designs a dual-path cross-modal retrieval scheme. For the content information of video and music, content features are extracted using a generic network model pre-trained on a large-scale dataset. The content features are refined using an encoding–decoding structure because of their mixed information and redundancy. An emotion extraction network is designed to extract features for the emotion information of videos and music, where the emotional discriminatory loss is used to optimize the feature extraction network. Compared with the content features extracted by the generic network model, the emotion feature information is more specific and less redundant, so the emotion common space with fully connected layer structure is designed to eliminate the heterogeneity of the two modalities and obtain the emotion shared representation. Finally, combining content information and emotion information, the fusion shared space is designed to obtain the final representation features of video and music, and the video and music retrieval task is completed by calculating the similarity between the representation features.As shown in There are two parts which compose the optimization loss for content common networks: reconstruction loss and metric loss. Reconstruction loss optimizes encoder dimensionality reduction processing for content features. Metric loss optimizes the consistency of common feature information for generated video and music content.Given video feature The metric loss:The content loss:As shown in Y(∙) is the real emotion label, and F(∙) denotes the emotion common representation space of the multilayer perceptron structure.There are two parts that compose the optimization loss for emotion common networks: distinguish loss and inter-modal loss. The distinguish loss adopts the cross-entropy loss function.Inter-modal loss preserves the difference in emotion characteristics of various modal data. This benefits the emotional common space to form pure emotional distinguish information from video and music.The emotion loss:The splicing fusion features are obtained by splicing two different features of the same modal data. As the similarity between the data is calculated, the information in the corresponding dimensions is calculated and accumulated separately. The splicing and fusion strategy can therefore be seen as a simple superposition between the similarities of the two feature dimensions. Therefore, on the basis of the splicing fusion, we have designed the fusion common space, where the splicing features obtain interactive fusion features through the fusion public space. This common space not only ensures that the heterogeneity of content and emotional information is further eliminated, but also that content and emotional information interact with each other, hence the choice of the fully connected layer (FC) in the design of the network structure. In FC, each neuron is formed with the participation of all neural units in the previous layer. This allows for comprehensive information transfer between features, thus ensuring that content information and emotional information can fully interact with each other.The loss function optimized for common fusion space is the loss of contrast between interactive fusion features:Combining Equations (3), (6) and (7), we obtain the objective function of the proposed method DPVM as:φ  using a fixed frame sampling scheme, and 16 consecutive frames are selected in each segment. The input video datum is To further reduce the redundancy of the video data in the emotion-sharing network, frames that contribute more to the emotion are selected, so the emotion keyframe scheme is chosen. The emotion keyframes scheme first detects the shot boundaries of the video based on the color histogram differences between frames and divides the shots one at a time. For each shot boundary frame, an emotion score is obtained by using a picture emotion recognition network model, setting an emotion score threshold and filtering out key shots. In each key shot, the frame with the largest inter-frame difference is selected as the key frame, which makes the difference between frames greater and further reduces redundancy. The final keyframe was set to 16, and the input video datum is −4, and the batch size is 64. Metric loss uses the calculation method of cosine distance. In (3), (6) and (8), We use Adam optimizer  with {β1To evaluate our method, we compare its performance with methods from related work. The benchmark network is the prior method of Prétet et al. , MLP is The results show the effectiveness of the DPVM network and the best results so far based on interaction fusion. In addition, replacing the classical metric loss with a polarity penalty metric loss has a significant improvement on the retrieval results.In order to verify the validity of the proposed content and emotional messages, ablation experiments are conducted for common spaces. Among them, In video–music retrieval work, we emphasize the importance of emotional information by designing a dual path network to integrate content information with emotional information. Due to the difference between content information and emotional information, we have various designs for feature extraction and common representation space. Specifically, the content information is extracted through the pre-trained network, and the content common space of the coding and decoding structure is designed to remove redundant information. The path of emotional information adopts the emotional scheme, and constructs the common space of emotional with the MLP as a structure. We also explored ways to integrate content with emotion. Furthermore, we optimized the classical metric loss function according to the characteristics of the task. Finally, we produce and publish an emotion-labeled video music retrieval dataset: MVED, which we hope will contribute to research in this area. As part of future work, we will continue to conduct in-depth research on the interaction between emotion and content. At the same time, in addition to movie videos, we are also exploring further for user-generated videos.