Emotions explain diferences in the difusion of true vs. false social media rumors

Pröllochs, N., Bär, D. & Feuerriegel, S.

Sci Rep 11, 22721 (2021).

https://doi.org/10.1038/s41598-021-01813-2

Abstract

False rumors (often termed “fake news”) on social media pose a signifcant threat to modern societies. However, potential reasons for the widespread difusion of false rumors have been underexplored. In this work, we analyze whether sentiment words, as well as diferent emotional words, in social media content explain diferences in the spread of true vs. false rumors. For this purpose, we collected N = 126,301 rumor cascades from Twitter, comprising more than 4.5 million retweets that have been fact-checked for veracity. We then categorized the language in social media content to (1) sentiment (i.e., positive vs. negative) and (2) eight basic emotions (i. e., anger, anticipation, disgust, fear, joy, trust, sadness, and surprise). We find that sentiment and basic emotions explain differences in the structural properties of true vs. false rumor cascades. False rumors (as compared to true rumors) are more likely to go viral if they convey a higher proportion of terms associated with a positive sentiment.  Further, false rumors are viral when embedding emotional words classifed as trust, anticipation, or anger. All else being equal, false rumors conveying one standard deviation more positive sentiment have a 37.58% longer lifetime and reach 61.44% more users. Our fndings ofer insights into how true vs. false rumors spread and highlight the importance of managing emotions in social media content.

Here are some thoughts:

This research analyzes how language used in social media posts influences the spread of rumors, specifically true vs. false ones. The study focuses on sentiment (positive vs. negative) and basic emotions (anger, anticipation, trust) in online content. They found that positive language and emotions like anger, anticipation, and trust are linked to a wider spread of false rumors. This is because emotions are known to influence how online content is shared.

Final thought: When we encounter a rumor that evokes emotions like anger, anticipation, or trust, it is more likely to stand out to us. This increased salience makes us more likely to share the rumor, even if it is false.

Understanding Moral Preferences Using Sentiment Analysis

Capraro, Valerio and Vanzo, Andrea
(May 28, 2018).

Abstract

Behavioral scientists have shown that people are not solely motivated by the economic consequences of the available actions, but they also care about the actions themselves. Several models have been proposed to formalize this preference for "doing the right thing". However, a common limitation of these models is their lack of predictive power: given a set of instructions of a decision problem, they lack to make clear predictions of people's behavior. Here, we show that, at least in simple cases, the overall qualitative pattern of behavior can be predicted reasonably well using a Computational Linguistics technique, known as Sentiment Analysis. The intuition is that people are reluctant to make actions that evoke negative emotions, and are eager to make actions that stimulate positive emotions. To show this point, we conduct an economic experiment in which decision-makers either get 50 cents, and another person gets nothing, or the opposite, the other person gets 50 cents and the decision maker gets nothing. We experimentally manipulate the wording describing the available actions using six words, from very negative (e.g., stealing) to very positive (e.g., donating) connotations. In agreement with our theory, we show that sentiment polarity has a U-shaped effect on pro-sociality. We also propose a utility function that can qualitatively predict the observed behavior, as well as previously reported framing effects. Our results suggest that building bridges from behavioral sciences to Computational Linguistics can help improve our understanding of human decision making.

The research is here.