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. The basic measure of virality in Twitter is the probability of retweet and we are interested in which dimensions of the content of a tweet leads to retweeting. We reconcile seemingly conflicting earlier findings in the literature and 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 training a classifier that can distinguish tweets that carry news and non-news information. We present evidence that negative sentiment does enhance virality in the news segment, but not in the non-news segment. Our findings may be summarized ’If you want to be retweeted: Sweet talk your friends or serve bad news to the public’.
Professor Lars Kai Hansen
Head of section, Danish Technical University, Informatics, Department of Informatics and Mathematical Modeling
Professor Hansen specializes in statistical machine learning with applications in bio-medicine and digital media. He is also involved in a number of commercial activities operationalizing his research, with a special interest in user interfaces and social media.