We take interest in the early assessment of risk for depression in social media users. We focus on the eRisk 2018 dataset, which represents users as a sequence of their written online contributions. We implement four RNN-based systems to classify the users. We explore several aggregations methods to combine predictions on individual posts. Our best model reads through all writings of a user in parallel but uses an attention mechanism to prioritize the most important ones at each timestep.
@cite_1 used a more classical approach to classify Twitter users as being at risk of depression or not. They first manually crafted features that describe users' online behavior and characterize their speech. The measures were computed daily, so a user is represented as the time series of the features. Then, the training and predictions were done by a svm with PCA for dimensionality reduction.
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We developed computational models to predict the emergence of depression and Post-Traumatic Stress Disorder in Twitter users. Twitter data and details of depression history were collected from 204 individuals (105 depressed, 99 healthy). We extracted predictive features measuring affect, linguistic style, and context from participant tweets (N = 279,951) and built models using these features with supervised learning algorithms. Resulting models successfully discriminated between depressed and healthy content, and compared favorably to general practitioners’ average success rates in diagnosing depression, albeit in a separate population. Results held even when the analysis was restricted to content posted before first depression diagnosis. State-space temporal analysis suggests that onset of depression may be detectable from Twitter data several months prior to diagnosis. Predictive results were replicated with a separate sample of individuals diagnosed with PTSD (Nusers = 174, Ntweets = 243,775). A state-space time series model revealed indicators of PTSD almost immediately post-trauma, often many months prior to clinical diagnosis. These methods suggest a data-driven, predictive approach for early screening and detection of mental illness. Major depression constitutes a serious challenge in personal and public health. Tens of millions of people each year suffer from depression and only a fraction receives adequate treatment. We explore the potential to use social media to detect and diagnose major depressive disorder in individuals. We first employ crowdsourcing to compile a set of Twitter users who report being diagnosed with clinical depression, based on a standard psychometric instrument. Through their social media postings over a year preceding the onset of depression, we measure behavioral attributes relating to social engagement, emotion, language and linguistic styles, ego network, and mentions of antidepressant medications. We leverage these behavioral cues, to build a statistical classifier that provides estimates of the risk of depression, before the reported onset. We find that social media contains useful signals for characterizing the onset of depression in individuals, as measured through decrease in social activity, raised negative affect, highly clustered egonetworks, heightened relational and medicinal concerns, and greater expression of religious involvement. We believe our findings and methods may be useful in developing tools for identifying the onset of major depression, for use by healthcare agencies; or on behalf of individuals, enabling those suffering from depression to be more proactive about their mental health. Social media and microblog tools are increasingly used by individuals to express their feelings and opinions in the form of short text messages. Detecting emotions in text has a wide range of applications including identifying anxiety or depression of individuals and measuring well-being or public mood of a community. In this paper, we propose a new approach for automatically classifying text messages of individuals to infer their emotional states. To model emotional states, we utilize the well-established Circumplex model that characterizes aective experience along two dimensions: valence and arousal. We select Twitter messages as input data set, as they provide a very large, diverse and freely avail- able ensemble of emotions. Using hash-tags as labels, our methodology trains supervised classiers to detect multiple classes of emotion on potentially huge data sets with no manual eort. We investigate the utility of several features for emotion detection, including unigrams, emoticons, negations and punctuations. To tackle the problem of sparse and high dimensional feature vectors of messages, we utilize a lexicon of emotions. We have compared the accuracy of several machine learning algorithms, including SVM, KNN, Decision Tree, and Naive Bayes for classifying Twitter messages. Our technique has an accuracy of over 90 , while demonstrating robustness across learning algorithms. This paper presents an approach to geolocating users of online social networks, based solely on their 'friendship' connections. We observe that users interact more regularly with those closer to themselves and hypothesise that, in many cases, a person's social network is sufficient to reveal their location. The geolocation problem is formulated as a classification task, where the most likely city for a user without an explicit location is chosen amongst the known locations of their social ties. Our method uses an SVM classifier and a number of features that reflect different aspects and characteristics of Twitter user networks. The SVM classifier is trained and evaluated on a dataset of Twitter users with known locations. Our method outperforms a state-of-the-art method for geolocating users based on their social ties. Religious belief plays an important role in how people behave, influencing how they form preferences, interpret events around them, and develop relationships with others. Traditionally, the religion labels of user population are obtained by conducting a large scale census study. Such an approach is both high cost and time consuming. In this paper, we study the problem of predicting users' religion labels using their microblogging data. We formulate religion label prediction as a classification task, and identify content, structure and aggregate features considering their self and social variants for representing a user. We introduce the notion of representative user to identify users who are important in the religious user community. We further define features using representative users. We show that SVM classifiers using our proposed features can accurately assign Christian and Muslim labels to a set of Twitter users with known religion labels. Social and information networking activities such as on Facebook, Twitter, WeChat, and Weibo have become an indispensable part of our everyday life, where we can easily access friends' behaviors and are in turn influenced by them. Consequently, an effective social influence prediction for each user is critical for a variety of applications such as online recommendation and advertising. Conventional social influence prediction approaches typically design various hand-crafted rules to extract user- and network-specific features. However, their effectiveness heavily relies on the knowledge of domain experts. As a result, it is usually difficult to generalize them into different domains. Inspired by the recent success of deep neural networks in a wide range of computing applications, we design an end-to-end framework, DeepInf, to learn users' latent feature representation for predicting social influence. In general, DeepInf takes a user's local network as the input to a graph neural network for learning her latent social representation. We design strategies to incorporate both network structures and user-specific features into convolutional neural and attention networks. Extensive experiments on Open Academic Graph, Twitter, Weibo, and Digg, representing different types of social and information networks, demonstrate that the proposed end-to-end model, DeepInf, significantly outperforms traditional feature engineering-based approaches, suggesting the effectiveness of representation learning for social applications. Eigentaste is a collaborative filtering algorithm that uses i>universal queries to elicit real-valued user ratings on a common set of items and applies principal component analysis (PCA) to the resulting dense subset of the ratings matrix. PCA facilitates dimensionality reduction for offline clustering of users and rapid computation of recommendations. For a database of i>n users, standard nearest-neighbor techniques require i>O(i>n) processing time to compute recommendations, whereas Eigentaste requires i>O(1) (constant) time. We compare Eigentaste to alternative algorithms using data from i>Jester, an online joke recommending system. Jester has collected approximately 2,500,000 ratings from 57,000 users. We use the Normalized Mean Absolute Error (NMAE) measure to compare performance of different algorithms. In the Appendix we use Uniform and Normal distribution models to derive analytic estimates of NMAE when predictions are random. On the Jester dataset, Eigentaste computes recommendations two orders of magnitude faster with no loss of accuracy. Jester is online at: http: eigentaste.berkeley.edu With the proliferation of social networks and blogs, the Internet is increasingly being used to disseminate personal health information rather than just as a source of information. In this paper we exploit the wealth of user-generated data, available through the micro-blogging service Twitter, to estimate and track the incidence of health conditions in society. The method is based on two stages: we start by extracting possibly relevant tweets using a set of specially crafted regular expressions, and then classify these initial messages using machine learning methods. Furthermore, we selected relevant features to improve the results and the execution times. To test the method, we considered four health states or conditions, namely flu, depression, pregnancy and eating disorders, and two locations, Portugal and Spain. We present the results obtained and demonstrate that the detection results and the performance of the method are improved after feature selection. The results are promising, with areas under the receiver operating characteristic curve between 0.7 and 0.9, and f-measure values around 0.8 and 0.9. This fact indicates that such approach provides a feasible solution for measuring and tracking the evolution of health states within the society. Our daily lives have been immersed in widespread location-based social networks (LBSNs). As an open platform, LBSNs typically allow all kinds of users to register accounts. Malicious attackers can easily join and post misleading information, often with the intention of influencing users' decisions in urban computing environments. To provide reliable information and improve the experience for legitimate users, we design and implement DeepScan, a malicious account detection system for LBSNs. Different from existing approaches, DeepScan leverages emerging deep learning technologies to learn users' dynamic behavior. In particular, we introduce the long short-term memory (LSTM) neural network to conduct time series analysis of user activities. DeepScan combines newly introduced time series features and a set of conventional features extracted from user activities, and exploits a supervised machine-learning-based model for detection. Using real traces collected from Dianping, a representative LBSN, we demonstrate that DeepScan can achieve excellent prediction performance with an F1-score of 0.964. We also find that the time series features play a critical role in the detection system. Hateful speech in Online Social Networks (OSNs) is a key challenge for companies and governments, as it impacts users and advertisers, and as several countries have strict legislation against the practice. This has motivated work on detecting and characterizing the phenomenon in tweets, social media posts and comments. However, these approaches face several shortcomings due to the noisiness of OSN data, the sparsity of the phenomenon, and the subjectivity of the definition of hate speech. This works presents a user-centric view of hate speech, paving the way for better detection methods and understanding. We collect a Twitter dataset of @math users along with up to @math tweets from their timelines with a random-walk-based crawler on the retweet graph, and select a subsample of @math to be manually annotated as hateful or not through crowdsourcing. We examine the difference between user activity patterns, the content disseminated between hateful and normal users, and network centrality measurements in the sampled graph. Our results show that hateful users have more recent account creation dates, and more statuses, and followees per day. Additionally, they favorite more tweets, tweet in shorter intervals and are more central in the retweet network, contradicting the "lone wolf" stereotype often associated with such behavior. Hateful users are more negative, more profane, and use less words associated with topics such as hate, terrorism, violence and anger. We also identify similarities between hateful normal users and their 1-neighborhood, suggesting strong homophily. OBJECTIVE.The goal was to assess the extent of unwanted and wanted exposure to online pornography among youth Internet users and associated risk factors. METHODS.A telephone survey of a nationally representative sample of 1500 youth Internet users aged 10 to 17 years was conducted between March and June 2005. RESULTS.Forty-two percent of youth Internet users had been exposed to online pornography in the past year. Of those, 66 reported only unwanted exposure. Multinomial logistic regression analysis was used to compare youth with unwanted exposure only or any wanted exposure with those with no exposure. Unwanted exposure was related to only 1 Internet activity, namely, using filesharing programs to download images. Filtering and blocking software reduced the risk of unwanted exposure, as did attending an Internet safety presentation by law enforcement personnel. Unwanted exposure rates were higher for teens, youth who reported being harassed or sexually solicited online or interpersonally victimized offline, and youth who scored in the borderline or clinically significant range on the Child Behavior Checklist subscale for depression. Wanted exposure rates were higher for teens, boys, and youth who used file-sharing programs to download images, talked online to unknown persons about sex, used the Internet at friends’ homes, or scored in the borderline or clinically significant range on the Child Behavior Checklist subscale for rule-breaking. Depression also could be a risk factor for some youth. Youth who used filtering and blocking software had lower odds of wanted exposure. CONCLUSIONS.More research concerning the potential impact of Internet pornography on youth is warranted, given the high rate of exposure, the fact that much exposure is unwanted, and the fact that youth with certain vulnerabilities, such as depression, interpersonal victimization, and delinquent tendencies, have more exposure. Speech emotion recognition (SER) is a challenging task since it is unclear what kind of features are able to reflect the characteristics of human emotion from speech. However, traditional feature extractions perform inconsistently for different emotion recognition tasks. Obviously, different spectrogram provides information reflecting difference emotion. This paper proposes a systematical approach to implement an effectively emotion recognition system based on deep convolution neural networks (DCNNs) using labeled training audio data. Specifically, the log-spectrogram is computed and the principle component analysis (PCA) technique is used to reduce the dimensionality and suppress the interferences. Then the PCA whitened spectrogram is split into non-overlapping segments. The DCNN is constructed to learn the representation of the emotion from the segments with labeled training speech data. Our preliminary experiments show the proposed emotion recognition system based on DCNNs (containing 2 convolution and 2 pooling layers) achieves about 40 classification accuracy. Moreover, it also outperforms the SVM based classification using the hand-crafted acoustic features. With the growing popularity and usage of online social media services, people now have accounts (some times several) on multiple and diverse services like Facebook, Linked In, Twitter and You Tube. Publicly available information can be used to create a digital footprint of any user using these social media services. Generating such digital footprints can be very useful for personalization, profile management, detecting malicious behavior of users. A very important application of analyzing users' online digital footprints is to protect users from potential privacy and security risks arising from the huge publicly available user information. We extracted information about user identities on different social networks through Social Graph API, Friend Feed, and Profilactic, we collated our own dataset to create the digital footprints of the users. We used username, display name, description, location, profile image, and number of connections to generate the digital footprints of the user. We applied context specific techniques (e.g. Jaro Winkler similarity, Word net based ontologies) to measure the similarity of the user profiles on different social networks. We specifically focused on Twitter and Linked In. In this paper, we present the analysis and results from applying automated classifiers for disambiguating profiles belonging to the same user from different social networks. User ID and Name were found to be the most discriminative features for disambiguating user profiles. Using the most promising set of features and similarity metrics, we achieved accuracy, precision and recall of 98 , 99 , and 96 , respectively. We analyze the dynamics of the behavior known as 'unfollow' in Twitter. We collected daily snapshots of the online relationships of 1.2 million Korean-speaking users for 51 days as well as all of their tweets. We found that Twitter users frequently unfollow. We then discover the major factors, including the reciprocity of the relationships, the duration of a relationship, the followees' informativeness, and the overlap of the relationships, which affect the decision to unfollow. We conduct interview with 22 Korean respondents to supplement the quantitative results. They unfollowed those who left many tweets within a short time, created tweets about uninteresting topics, or tweeted about the mundane details of their lives. To the best of our knowledge, this work is the first systematic study of the unfollow behavior in Twitter. Increasing evidence suggests that a growing amount of social media content is generated by autonomous entities known as social bots. In this work we present a framework to detect such entities on Twitter. We leverage more than a thousand features extracted from public data and meta-data about users: friends, tweet content and sentiment, network patterns, and activity time series. We benchmark the classification framework by using a publicly available dataset of Twitter bots. This training data is enriched by a manually annotated collection of active Twitter users that include both humans and bots of varying sophistication. Our models yield high accuracy and agreement with each other and can detect bots of different nature. Our estimates suggest that between 9 and 15 of active Twitter accounts are bots. Characterizing ties among accounts, we observe that simple bots tend to interact with bots that exhibit more human-like behaviors. Analysis of content flows reveals retweet and mention strategies adopted by bots to interact with different target groups. Using clustering analysis, we characterize several subclasses of accounts, including spammers, self promoters, and accounts that post content from connected applications. Twitter has increasingly become a popular platform to share news and user opinion. A tweet is considered to be important if it receives high number of affirmative reactions from other Twitter users via Retweets. Retweet count is thus considered as a surrogate measure for positive crowd-sourced reactions - high number of retweets of a tweet not only help the tweet being broadcasted, but also aid in making its topic trending. This in turn bolsters the social reputation of the author of the tweet. Since social reputation impact of users t weets influences many decisions (such as promoting brands, advertisement etc.), several blackmarket syndicates have actively been engaged in producing fake retweets in a collusive manner. Users who want to boost the impact of their tweets approach the blackmarket services, and gain retweets for their own tweets by retweeting other customers' tweets. Thus they become customers of blackmarket syndicates and engage in fake activities. Interestingly, these customers are neither bots, nor even fake users - they are usually normal human beings; they express a mix of organic and inorganic retweeting activities, and there is no synchronicity across their behaviors. In this paper, we make a first attempt to investigate such blackmarket customers engaged in producing fake retweets. We collected and annotated a novel dataset comprising of customers of many blackmarket services and characterize them using a set of 64 novel features. We show how their social behavior differs from genuine users. We then use state-of-the-art supervised models to detect three types of customers (bots, promotional, normal) and genuine users. We achieve a Macro Fl-score of 0.87 with SVM, outperforming four other baselines significantly. We further design a browser extension, SCoRe which, given the link of a tweet, spots its fake retweeters in real-time. We also collected users' feedback on the performance of SCoRe and obtained 85 accuracy. Online social networks pose an interesting problem: how to best characterize the different classes of user behavior. Traditionally, user behavior characterization methods, based on user individual features, are not appropriate for online networking sites. In these environments, users interact with the site and with other users through a series of multiple interfaces that let them to upload and view content, choose friends, rank favorite content, subscribe to users and do many other interactions. Different interaction patterns can be observed for different groups of users. In this paper, we propose a methodology for characterizing and identifying user behaviors in online social networks. First, we crawled data from YouTube and used a clustering algorithm to group users that share similar behavioral pattern. Next, we have shown that attributes that stem from the user social interactions, in contrast to attributes relative to each individual user, are good discriminators and allow the identification of relevant user behaviors. Finally, we present and discuss experimental results of the use of proposed methodology. A set of useful profiles, derived from the analysis of the YouTube sample is presented. The identification of different classes of user behavior has the potential to improve, for instance, recommendation systems for advertisements in online social networks. We study how contract element extraction can be automated. We provide a labeled dataset with gold contract element annotations, along with an unlabeled dataset of contracts that can be used to pre-train word embeddings. Both datasets are provided in an encoded form to bypass privacy issues. We describe and experimentally compare several contract element extraction methods that use manually written rules and linear classifiers (logistic regression, SVMs) with hand-crafted features, word embeddings, and part-of-speech tag embeddings. The best results are obtained by a hybrid method that combines machine learning (with hand-crafted features and embeddings) and manually written post-processing rules. Social bots are automatic or semi-automatic computer programs that mimic humans and or human behavior in online social networks. Social bots can attack users (targets) in online social networks to pursue a variety of latent goals, such as to spread information or to influence targets. Without a deep understanding of the nature of such attacks or the susceptibility of users, the potential of social media as an instrument for facilitating discourse or democratic processes is in jeopardy. In this paper, we study data from the Social Bot Challenge 2011 - an experiment conducted by the WebEcologyProject during 2011 - in which three teams implemented a number of social bots that aimed to influence user behavior on Twitter. Using this data, we aim to develop models to (i) identify susceptible users among a set of targets and (ii) predict users’ level of susceptibility. We explore the predictiveness of three different groups of features (network, behavioral and linguistic features) for these tasks. Our results suggest that susceptible users tend to use Twitter for a conversational purpose and tend to be more open and social since they communicate with many different users, use more social words and show more affection than non-susceptible users. Once every five minutes, Twitter publishes a list of trending topics by monitoring and analyzing tweets from its users. Similarly, Google makes available hourly a list of hot queries that have been issued to the search engine. We claim that social trends fired by Twitter may help explain and predict web trends derived from Google. Indeed, we argue that information flooding nearly real-time across the Twitter social network could anticipate the set of topics that users will later search on the Web. In this work, we analyze the time series derived from the daily volume index of each trend, either by Twitter or Google. Our study on a real-world dataset reveals that about 26 of the trending topics raising from Twitter “asis” are also found as hot queries issued to Google. Also, we find that about 72 of the similar trends appear first on Twitter. Thus, we assess the relation between comparable Twitter and Google trends by testing three classes of time series regression models. First, we find that Google by its own is not able to effectively predict the time behavior of its trends. Indeed, we show that autoregressive models, which try to fit time series of Google trends, perform poorly. On the other hand, we validate the forecasting power of Twitter by showing that models, which use Google as the dependent variable and Twitter as the explanatory variable, retain as significant the past values of Twitter 60 of times. Moreover, we discover that a Twitter trend causes as imilar Google trend to later occur about 43 of times. In the end, we show that the very best-performing models are those using past values of both Twitter and Google.