Good Friends, Bad News - Affect and Virality in Twitter

The link between affect, defined as the capacity for sentimental arousal on the part of a message, and virality, defined as the probability that it be sent along, is of significant theoretical and practical importance, e.g. for viral marketing. A quantitative study of emailing of articles from the NY Times finds a strong link between positive affect and virality, and, based on psychological theories it is concluded that this relation is universally valid. The conclusion appears to be in contrast with classic theory of diffusion in news media emphasizing negative affect as promoting propagation. In this paper we explore the apparent paradox in a quantitative analysis of information diffusion on Twitter. Twitter is interesting in this context as it has been shown to present both the characteristics social and news media. The basic measure of virality in Twitter is the probability of retweet. Twitter is different from email in that retweeting does not depend on pre-existing social relations, but often occur among strangers, thus in this respect Twitter may be more similar to traditional news media. We therefore hypothesize that negative news content is more likely to be retweeted, while for non-news tweets positive sentiments support virality. To test the hypothesis we analyze three corpora: A complete sample of tweets about the COP15 climate summit, a random sample of tweets, and a general text corpus including news. The latter allows us to train a classifier that can distinguish tweets that carry news and non-news information. We present evidence that negative sentiment enhances virality in the news segment, but not in the non-news segment. We conclude that the relation between affect and virality is more complex than expected based on the findings of Berger and Milkman (2010), in short 'if you want to be cited: Sweet talk your friends or serve bad news to the public'.Comment: 14 pages, 1 table. Submitted to The 2011 International Workshop on Social Computing, Network, and Services (SocialComNet 2011

Scalable Privacy-Compliant Virality Prediction on Twitter

The digital town hall of Twitter becomes a preferred medium of communication for individuals and organizations across the globe. Some of them reach audiences of millions, while others struggle to get noticed. Given the impact of social media, the question remains more relevant than ever: how to model the dynamics of attention in Twitter. Researchers around the world turn to machine learning to predict the most influential tweets and authors, navigating the volume, velocity, and variety of social big data, with many compromises. In this paper, we revisit content popularity prediction on Twitter. We argue that strict alignment of data acquisition, storage and analysis algorithms is necessary to avoid the common trade-offs between scalability, accuracy and privacy compliance. We propose a new framework for the rapid acquisition of large-scale datasets, high accuracy supervisory signal and multilanguage sentiment prediction while respecting every privacy request applicable. We then apply a novel gradient boosting framework to achieve state-of-the-art results in virality ranking, already before including tweet's visual or propagation features. Our Gradient Boosted Regression Tree is the first to offer explainable, strong ranking performance on benchmark datasets. Since the analysis focused on features available early, the model is immediately applicable to incoming tweets in 18 languages.Comment: AffCon@AAAI-19 Best Paper Award; Presented at AAAI-19 W1: Affective Content Analysi

Attractability and Virality: The Role of Message Features and Social Influence in Health News Diffusion

What makes health news articles attractable and viral? Why do some articles diffuse widely by prompting audience selections (attractability) and subsequent social retransmissions (virality), while others do not? Identifying what drives social epidemics of health news coverage is crucial to our understanding of its impact on the public, especially in the emerging media environment where news consumption has become increasingly selective and social. This dissertation examines how message features and social influence affect the volume and persistence of attractability and virality within the context of the online diffusion of New York Times (NYT) health news articles. The dissertation analyzes (1) behavioral data of audience selections and retransmissions of the NYT articles and (2) associated article content and context data that are collected using computational social science approaches (automated data mining; computer-assisted content analysis) along with more traditional methods (manual content analysis; message evaluation survey). Analyses of message effects on the total volume of attractability and virality show that articles with high informational utility and positive sentiment invite more frequent selections and retransmissions, and that articles are also more attractable when presenting controversial, emotionally evocative, and familiar content. Furthermore, these analyses reveal that informational utility and novelty have stronger positive associations with email-specific virality, while emotion-related message features, content familiarity, and exemplification play a larger role in triggering social media-based retransmissions. Temporal dynamics analyses demonstrate social influence-driven cumulative advantage effects, such that articles which stay on popular-news lists longer invite more frequent subsequent selections and retransmissions. These analyses further show that the social influence effects are stronger for articles containing message features found to enhance the total volume of attractability and virality. This suggests that those synergistic interactions might underlie the observed message effects on total selections and retransmissions. Exploratory analyses reveal that the effects of social influence and message features tend to be similar for both (1) the volume of audience news selections and retransmissions and (2) the persistence of those behaviors. However, some message features, such as expressed emotionality, are relatively unique predictors of persistence outcomes. Results are discussed in light of their implications for communication research and practice

On the relation between message sentiment and its virality on social media

We investigate the relation between the sentiment of a message on social media and its virality, defined as the volume and speed of message diffusion. We analyze 4.1 million messages (tweets) obtained from Twitter. Although factors affecting message diffusion on social media have been studied previously, we focus on message sentiment and reveal how the polarity of message sentiment affects its virality. The virality of a message is characterized by the number of message repostings (retweets) and the time elapsed from the original posting of a message to its Nth reposting (N-retweet time). Through extensive analysis using the 4.1 million tweets and their retweets in 1 week, we discover that negative messages are likely to be reposted more rapidly and frequently than positive and neutral messages. Specifically, the reposting volume of negative messages is 20–60% higher than that of positive and neutral messages, and negative messages spread 25% faster than positive and neutral messages when the diffusion volume is quite high. We also perform longitudinal analysis of message diffusion observed over 1 year and find that recurrent diffusion of negative messages is less frequent than that of positive and neutral messages. Moreover, we present a simple message diffusion model that can reproduce the characteristics of message diffusion observed in this paper

Breaking the News: First Impressions Matter on Online News

A growing number of people are changing the way they consume news, replacing the traditional physical newspapers and magazines by their virtual online versions or/and weblogs. The interactivity and immediacy present in online news are changing the way news are being produced and exposed by media corporations. News websites have to create effective strategies to catch people's attention and attract their clicks. In this paper we investigate possible strategies used by online news corporations in the design of their news headlines. We analyze the content of 69,907 headlines produced by four major global media corporations during a minimum of eight consecutive months in 2014. In order to discover strategies that could be used to attract clicks, we extracted features from the text of the news headlines related to the sentiment polarity of the headline. We discovered that the sentiment of the headline is strongly related to the popularity of the news and also with the dynamics of the posted comments on that particular news.Comment: The paper appears in ICWSM 201