This article provides a comprehensive investigation on the relations between virality of news articles and the emotions they are found to evoke. Virality, in our view, is a phenomenon with many facets, i.e. under this generic term several different effects of persuasive communication are comprised. By exploiting a high-coverage and bilingual corpus of documents containing metrics of their spread on social networks as well as a massive affective annotation provided by readers, we present a thorough analysis of the interplay between evoked emotions and viral facets. We highlight and discuss our findings in light of a cross-lingual approach: while we discover differences in evoked emotions and corresponding viral effects, we provide preliminary evidence of a generalized explanatory model rooted in the deep structure of emotions: the Valence-Arousal-Dominance (VAD) circumplex. We find that viral facets appear to be consistently affected by particular VAD configurations, and these configurations indicate a clear connection with distinct phenomena underlying persuasive communication.
@cite_27 , the authors focused on the role of emotions in the context of word-of-mouth marketing, using a dataset of Google+ posts: their analyses show consistency with @cite_0 , with increase in linked to higher likelihood of reshares, while the opposite trend holds for . @cite_17 looked at diffusion patterns on the very popular chinese platform Weibo, which shares many features with Twitter, and again found a similar result: angry posts appear to spread at a significantly faster rate, while sad posts do so at a significant lower rate.
@cite_0 @cite_27 @cite_17
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Recent years have witnessed the tremendous growth of the online social media. In China, Weibo, a Twitter-like service, has attracted more than 500 million users in less than five years. Connected by online social ties, different users might share similar affective states. We find that the correlation of anger among users is significantly higher than that of joy. While the correlation of sadness is surprisingly low. Moreover, there is a stronger sentiment correlation between a pair of users if they share more interactions. And users with larger number of friends possess more significant sentiment correlation with their neighborhoods. Our findings could provide insights for modeling sentiment influence and propagation in online social networks. A key aspect of word of mouth marketing are emotions. Emotions in texts help propagating messages in conventional advertising. In word of mouth scenarios, emotions help to engage consumers and incite to propagate the message further. While the function of emotions in offline marketing in general and word of mouth marketing in particular is rather well understood, online marketing can only offer a limited view on the function of emotions. In this contribution we seek to close this gap. We therefore investigate how emotions function in social media. To do so, we collected more than 30,000 brand marketing messages from the Google+ social networking site. Using state of the art computational linguistics classifiers, we compute the sentiment of these messages. Starting out with Poisson regression-based baseline models, we seek to replicate earlier findings using this large data set. We extend upon earlier research by computing multi-level mixed effects models that compare the function of emotions across different industries. We find that while the well known notion of activating emotions propagating messages holds in general for our data as well. But there are significant differences between the observed industries. As a new communication paradigm, social media has promoted information dissemination in social networks. Previous research has identified several content-related features as well as user and network characteristics that may drive information diffusion. However, little research has focused on the relationship between emotions and information diffusion in a social media setting. In this paper, we examine whether sentiment occurring in social media content is associated with a user's information sharing behavior. We carry out our research in the context of political communication on Twitter. Based on two data sets of more than 165,000 tweets in total, we find that emotionally charged Twitter messages tend to be retweeted more often and more quickly compared to neutral ones. As a practical implication, companies should pay more attention to the analysis of sentiment related to their brands and products in social media communication as well as in designing advertising content that triggers emotions. Recent years have witnessed the explosive growth of online social media. Weibo, a Twitter-like online social network in China, has attracted more than 300 million users in less than three years, with more than 1000 tweets generated in every second. These tweets not only convey the factual information, but also reflect the emotional states of the authors, which are very important for understanding user behaviors. However, a tweet in Weibo is extremely short and the words it contains evolve extraordinarily fast. Moreover, the Chinese corpus of sentiments is still very small, which prevents the conventional keyword-based methods from being used. In light of this, we build a system called MoodLens, which to our best knowledge is the first system for sentiment analysis of Chinese tweets in Weibo. In MoodLens, 95 emoticons are mapped into four categories of sentiments, i.e. angry, disgusting, joyful, and sad, which serve as the class labels of tweets. We then collect over 3.5 million labeled tweets as the corpus and train a fast Naive Bayes classifier, with an empirical precision of 64.3 . MoodLens also implements an incremental learning method to tackle the problem of the sentiment shift and the generation of new words. Using MoodLens for real-time tweets obtained from Weibo, several interesting temporal and spatial patterns are observed. Also, sentiment variations are well captured by MoodLens to effectively detect abnormal events in China. Finally, by using the highly efficient Naive Bayes classifier, MoodLens is capable of online real-time sentiment monitoring. The demo of MoodLens can be found at http: goo.gl 8DQ65. The rise of micro-blogging in recent years has resulted in significant access to emotion-laden text. Unlike emotion expressed in other textual sources (e.g., blogs, quotes in newswire, email, product reviews, or even clinical text), micro-blogs differ by (1) placing a strict limit on length, resulting radically in new forms of emotional expression, and (2) encouraging users to express their daily thoughts in real-time, often resulting in far more emotion statements than might normally occur. In this paper, we introduce a corpus collected from Twitter with annotated micro-blog posts (or “tweets”) annotated at the tweet-level with seven emotions: ANGER, DISGUST, FEAR, JOY, LOVE, SADNESS, and SURPRISE. We analyze how emotions are distributed in the data we annotated and compare it to the distributions in other emotion-annotated corpora. We also used the annotated corpus to train a classifier that automatically discovers the emotions in tweets. In addition, we present an analysis of the linguistic style used for expressing emotions our corpus. We hope that these observations will lead to the design of novel emotion detection techniques that account for linguistic style and psycholinguistic theories. The increasing adoption of social media provides unprecedented opportunities to gain insight into human nature at vastly broader scales. Regarding the study of population-wide sentiment, prior research commonly focuses on text-based analyses and ignores a treasure trove of sentiment-laden content: images. In this paper, we make methodological and computational contributions by introducing the Smile Index as a formalized measure of societ al happiness. Detecting smiles in 9 million geo-located tweets over 16 months, we validate our Smile Index against both text-based techniques and self-reported happiness. We further make observational contributions by applying our metric to explore temporal trends in sentiment, relate public mood to societ al events, and predict economic indicators. Reflecting upon the innate, language-independent aspects of facial expressions, we recommend future improvements and applications to enable robust, global-level analyses. We conclude with implications for researchers studying and facilitating the expression of collective emotion through socio-technical systems. Previous studies have established the link between one's actions (e.g., engaging with others vs. minding one's own business) and one's national culture (e.g., collectivist vs. individualistic), and such actions have been shown to be important as they are collectively affiliated with a country's economic outcomes (e.g., Gross Domestic Product). Hitherto there has not been any systematic study of whether one's action on Twitter (e.g., deciding when to post messages) is linked to one's culture (e.g., country's Pace of Life). To fix that, we build different network snapshots starting from 55,000 seed users on Twitter, and we do so for 10 weeks across 30 countries (after filtering those with low penetration rates) for a total of 2.34 M profiles. Based on Hofstede's theory of cultural dimensions and Levine's Pace of Life theory, we consider three behavioral patterns on Twitter (i.e., temporal predictability of tweets, engaging with others, and supporting others who are less popular) and associate them with three different dimensions derived from the two theories: Pace of Life, Individualism and Power Distance. We find the following strong correlations: activity predictability negatively correlates with Pace of Life (r=-0.62), tweets with mentions negatively correlates with Individualism (r = -0.55), and power (e.g, Twitter popularity) imbalance in relationships (between, for example, two users mentioning each other) is correlated with Power Distance (r=0.62). These three cultural dimensions matter because they are associated with a country's socio-economic aspects - with GDP per capita, income inequality, and education expenditure. We study the propagation of expression of moods from one individual to another in social media. Specifically, we utilize an ensemble of more than 200 moods, and follow their trails of retweets on Twitter. We examine the diffusion of these moods through the characteristics of the author of the retweeted post, of the post, and its linguistic style and topical content. We observe that moods of high valence and low to moderate activation propagate the most. Our findings also indicate that mood-bearing posts from individuals of medium #followers show highest diffusion, instead of elite users. Further, moods in postings with high self-attentional focus, posted by socially interactive women, and with fewer links diffuse the most. Finally, we leverage these characteristics to build a linear regression model that can predict extent of diffusion in a mood-bearing post. The model yields accuracy of 75 showing that user's attributes as well as post's language characteristics are key factors driving the diffusion of moods in social media. Social media services, such as Twitter, enable commercial businesses to participate actively in online word-of-mouth communication. In this project, we examined the potential influences of business engagement in online word-of-mouth communication on the level of consumers’ engagement and investigated the trajectories of a business’ online word-of-mouth message diffusion in the Twitter community. We used path analysis to examine 164,478 tweets from 96,725 individual Twitter users with regards to nine brands during a 5-week study period. We operationalized business engagement as the amount of online word-of-mouth messages from brand and the number of consumers the brand follows. We operationalized consumers’ engagement as the number of online word-of-mouth messages from consumers both connecting to the brand and having no connection with the brand as well as the number of consumers following the brand. We concluded that the business engagement on Twitter relates directly to consumers’ engagement with online word-of-mouth communication. In addition, retweeting, as an explicit way to show consumers’ response to business engagement, indicates that the influence only reaches consumers with a second-degree relationship to the brand and that the life cycle of a tweet is generally 1.5 to 4 hours at most. Our research has critical implications in terms of advancing the understanding of the business’s role in the online word-of-mouth communication and bringing insight to the analytics of social networks and online word-of-mouth message diffusion patterns. We propose a supervised model for expanding an opinion lexicon for Twitter.We combine automatically annotated tweets with existing hand-made opinion lexicons.We use POS tags and associations between words and sentiment as word-level features.Expanded words are mapped to a positive, negative, and neutral distribution.We outperform the performance obtained by using PMI semantic orientation alone. Opinion lexicons, which are lists of terms labeled by sentiment, are widely used resources to support automatic sentiment analysis of textual passages. However, existing resources of this type exhibit some limitations when applied to social media messages such as tweets (posts in Twitter), because they are unable to capture the diversity of informal expressions commonly found in this type of media.In this article, we present a method that combines information from automatically annotated tweets and existing hand-made opinion lexicons to expand an opinion lexicon in a supervised fashion. The expanded lexicon contains part-of-speech (POS) disambiguated entries with a probability distribution for positive, negative, and neutral polarity classes, similarly to SentiWordNet.To obtain this distribution using machine learning, we propose word-level attributes based on (a) the morphological information conveyed by POS tags and (b) associations between words and the sentiment expressed in the tweets that contain them. We consider tweets with both hard and soft sentiment labels. The sentiment associations are modeled in two different ways: using point-wise-mutual-information semantic orientation (PMI-SO), and using stochastic gradient descent semantic orientation (SGD-SO), which learns a linear relationship between words and sentiment. The training dataset is labeled by a seed lexicon formed by combining multiple hand-annotated lexicons.Our experimental results show that our method outperforms the three-dimensional word-level polarity classification performance obtained by using PMI-SO alone. This is significant because PMI-SO is a state-of-the-art measure for establishing world-level sentiment. Additionally, we show that lexicons created with our method achieve significant improvements over SentiWordNet for classifying tweets into polarity classes, and also outperform SentiStrength in the majority of the experiments. We analyze data from nearly 2 million text messages (tweets) across over 16,000 users on Twitter to examine differences between Christians and atheists in natural language. Analyses reveal that Christians use more positive emotion words and less negative emotion words than atheists. Moreover, two independent paths predict differences in expressions of happiness: frequency of words related to an intuitive (vs. analytic) thinking style and frequency of words related to social relationships. These findings provide the first evidence that the relationship between religion and happiness is partially mediated by thinking style. This research also provides support for previous laboratory studies and self-report data, suggesting that social connection partially mediates the relationship between religiosity and happiness. Implications for theory and the future of social science using computational methods to analyze social media are discussed. This paper examines the task of detecting intensity of emotion from text. We create the first datasets of tweets annotated for anger, fear, joy, and sadness intensities. We use a technique called best--worst scaling (BWS) that improves annotation consistency and obtains reliable fine-grained scores. We show that emotion-word hashtags often impact emotion intensity, usually conveying a more intense emotion. Finally, we create a benchmark regression system and conduct experiments to determine: which features are useful for detecting emotion intensity, and, the extent to which two emotions are similar in terms of how they manifest in language. In this paper we report research results investigating microblogging as a form of electronic word-of-mouth for sharing consumer opinions concerning brands. We analyzed more than 150,000 microblog postings containing branding comments, sentiments, and opinions. We investigated the overall structure of these microblog postings, the types of expressions, and the movement in positive or negative sentiment. We compared automated methods of classifying sentiment in these microblogs with manual coding. Using a case study approach, we analyzed the range, frequency, timing, and content of tweets in a corporate account. Our research findings show that 19p of microblogs contain mention of a brand. Of the branding microblogs, nearly 20p contained some expression of brand sentiments. Of these, more than 50p were positive and 33p were critical of the company or product. Our comparison of automated and manual coding showed no significant differences between the two approaches. In analyzing microblogs for structure and composition, the linguistic structure of tweets approximate the linguistic patterns of natural language expressions. We find that microblogging is an online tool for customer word of mouth communications and discuss the implications for corporations using microblogging as part of their overall marketing strategy. © 2009 Wiley Periodicals, Inc. We present a weakly supervised approach for learning hashtags, hashtag patterns, and phrases associated with five emotions: AFFECTION, ANGER RAGE, FEAR ANXIETY, JOY, and SADNESS DISAPPOINTMENT. Starting with seed hashtags to label an initial set of tweets, we train emotion classifiers and use them to learn new emotion hashtags and hashtag patterns. This process then repeats in a bootstrapping framework. Emotion phrases are also extracted from the learned hashtags and used to create phrase-based emotion classifiers. We show that the learned set of emotion indicators yields a substantial improvement in F-scores, ranging from + 5 to + 18 over baseline classifiers. Rapid growth of blogs in the Web 2.0 and the handshaking between multilingual search and sentiment analysis motivate us to develop a blog based emotion analysis system for Bengali. The present paper describes the identification, visualization and tracking of bloggers' emotions with respect to time from Bengali blog documents. A simple pre-processing technique has been employed to retrieve and store the bloggers' comments on specific topics. The assignment of Ekman's six basic emotions to the bloggers' comments is carried out at word, sentence and paragraph level granularities using the Bengali WordNet AffectLists. The evaluation produces the precision, recall and F-Score of 59.36 , 64.98 and 62.17 respectively for 1100 emotional comments retrieved from 20 blog documents. Each of the bloggers' emotions with respect to different timestamps is visualized by an emotion graph. The emotion graphs of 20 bloggers demonstrate that the system performs satisfactorily in case of emotion tracking. A huge number of informal messages are posted every day in social network sites, blogs, and discussion forums. Emotions seem to be frequently important in these texts for expressing friendship, showing social support or as part of online arguments. Algorithms to identify sentiment and sentiment strength are needed to help understand the role of emotion in this informal communication and also to identify inappropriate or anomalous affective utterances, potentially associated with threatening behavior to the self or others. Nevertheless, existing sentiment detection algorithms tend to be commercially oriented, designed to identify opinions about products rather than user behaviors. This article partly fills this gap with a new algorithm, SentiStrength, to extract sentiment strength from informal English text, using new methods to exploit the de facto grammars and spelling styles of cyberspace. Applied to MySpace comments and with a lookup table of term sentiment strengths optimized by machine learning, SentiStrength is able to predict positive emotion with 60.6p accuracy and negative emotion with 72.8p accuracy, both based upon strength scales of 1–5. The former, but not the latter, is better than baseline and a wide range of general machine learning approaches. © 2010 Wiley Periodicals, Inc. Detecting emotions in microblogs and social media posts has applications for industry, health, and security. Statistical, supervised automatic methods for emotion detection rely on text that is labeled for emotions, but such data are rare and available for only a handful of basic emotions. In this article, we show that emotion-word hashtags are good manual labels of emotions in tweets. We also propose a method to generate a large lexicon of word-emotion associations from this emotion-labeled tweet corpus. This is the first lexicon with real-valued word-emotion association scores. We begin with experiments for six basic emotions and show that the hashtag annotations are consistent and match with the annotations of trained judges. We also show how the extracted tweet corpus and word-emotion associations can be used to improve emotion classification accuracy in a different nontweet domain. The new social media such as Twitter and Sina Weibo has become an increasingly popular channel for spreading influence, challenging traditional media such as TVs and newspapers. The most influential and verified users, also called big-V accounts on Sina Weibo often attract million of followers and fans, creating massive “celebrity-centric” social networks on the social media, which play a key role in disseminating breaking news, latest events, and controversial opinions on social issues. Given the importance of these accounts, it is very crucial to understand social networks and user influence of these accounts and profile their followers' behaviors. Towards this end, this paper monitors a selected group of influential users on Sina Weibo and collects their tweet streams as well as retweeting and commenting activities on these tweets from their followers. Our analysis on tweet data streams from Sina Weibo reveals when and what the followers comment on the tweets of these influential users, and discovers different temporal patterns and word diversity in the comments. Based on the insight gained from follower characteristics, we further develop simple and intuitive algorithms for classifying the followers into spammers and normal fans. Our experimental results demonstrate that the proposed algorithms are able to achieve an average accuracy of 95.20 in detecting spammers from the followers who have commented on the tweets of these influential accounts. We perform a sentiment analysis of all tweets published on the microblogging platform Twitter in the second half of 2008. We use a psychometric instrument to extract six mood states (tension, depression, anger, vigor, fatigue, confusion) from the aggregated Twitter content and compute a six-dimensional mood vector for each day in the timeline. We compare our results to a record of popular events gathered from media and sources. We find that events in the social, political, cultural and economic sphere do have a significant, immediate and highly specific effect on the various dimensions of public mood. We speculate that large scale analyses of mood can provide a solid platform to model collective emotive trends in terms of their predictive value with regards to existing social as well as economic indicators. In this article, we analyze and compare user behavior on two different microblogging platforms: (1) Sina Weibo which is the most popular microblogging service in China and (2) Twitter. Such a comparison has not been done before at this scale and is therefore essential for understanding user behavior on microblogging services. In our study, we analyze more than 40 million microblogging activities and investigate microblogging behavior from different angles. We (i) analyze how people access microblogs and (ii) compare the writing style of Sina Weibo and Twitter users by analyzing textual features of microposts. Based on semantics and sentiments that our user modeling framework extracts from English and Chinese posts, we study and compare (iii) the topics and (iv) sentiment polarities of posts on Sina Weibo and Twitter. Furthermore, (v) we investigate the temporal dynamics of the microblogging behavior such as the drift of user interests over time. Our results reveal significant differences in the microblogging behavior on Sina Weibo and Twitter and deliver valuable insights for multilingual and culture-aware user modeling based on microblogging data. We also explore the correlation between some of these differences and cultural models from social science research.