This paper analyses the communicative and epistemic value of retweeting (and more generally of reposting content on social media). Against a naïve view, it argues that retweets are not acts of endorsement, motivating this diagnosis with linguistic data. Retweeting is instead modelled as a peculiar form of quotation, in which the reported content is indicated rather than reproduced. A relevance-theoretic account of the communicative import of retweeting is then developed, to spell out the complex mechanisms by which (...) retweets achieve their communicative goals. The last section outlines the epistemic threats posed by the increasing prevalence of retweeting on social media, linking them to the low reputational, cognitive and practical costs linked to this emerging form of communication. (shrink)

Social media platforms allow users to perform different speech acts: status updates could be assertives, a like is an expressive, a friendship request is a directive, and so on. But sharing (or "retweeting") seems to lack a fixed illocutive status: this explains why present controversies concerning the sharing of misinformation have been debated in legal procedure and discussed from the point of view of personal responsibility without reaching a general consensus. The premise of this paper is that the diffusion (...) of false or unwarranted information could be better analyzed if we consider sharing a precisely definable speech act. I will describe some dominant interpretations of the act of sharing that are not, however, sufficient to fully explain it. As an alternative, it will be shown that there is a specific illocutive structure of the act of sharing, which not only consists in asserting the "shareworthiness" or the relevance of a content, but is primarily comparable to an "attention-orienting" directive. (shrink)

Twitter makes conversation into something like a game. It scores our communication, giving us vivid and quantified feedback, via Likes, Retweets, and Follower counts. But this gamification doesn’t just increase our motivation to communicate; it changes the very nature of the activity. Games are more satisfying than ordinary life precisely because game-goals are simpler, cleaner, and easier to apply. Twitter is thrilling precisely because its goals have been artificially clarified and narrowed. When we buy into Twitter’s gamification, then our values (...) shift from the complex and pluralistic values of communication, to the narrower quest for popularity and virality. Twitter’s gamification bears some resemblance with the phenomena of echo chambers and moral outrage porn. In all these phenomena, we are instrumentalizing our ends for hedonistic reasons. We have shifted our aims in an activity, not because the new aims are more valuable, but in exchange for extra pleasure. (shrink)

We present a novel model of individual people, online posts, and media platforms to explain the online spread of epistemically toxic content such as fake news and suggest possible responses. We argue that a combination of technical features, such as the algorithmically curated feed structure, and social features, such as the absence of stable social-epistemic norms of posting and sharing in social media, is largely responsible for the unchecked spread of epistemically toxic content online. Sharing constitutes a distinctive communicative act, (...) governed by a dedicated norm and motivated to a large extent by social identity maintenance. But confusion about this norm and its lack of inherent epistemic checks lead readers to misunderstand posts, attribute excess or insufficient credibility to posts, and allow posters to evade epistemic accountability—all contributing to the spread of epistemically toxic content online. This spread can be effectively addressed if people and platforms add significantly more context to shared posts and platforms nudge people to develop and follow recognized epistemic norms of posting and sharing. (shrink)

Many philosophers hold that generics (i.e., unquantified generalizations) are pervasive in communication and that when they are about social groups, this may offend and polarize people because generics gloss over variations between individuals. Generics about social groups might be particularly common on Twitter (X). This remains unexplored, however. Using machine learning (ML) techniques, we therefore developed an automatic classifier for social generics, applied it to 1.1 million tweets about people, and analyzed the tweets. While it is often suggested that generics (...) are ubiquitous in everyday communication, we found that most tweets (78%) about people contained no generics. However, tweets with generics received more “likes” and retweets. Furthermore, while recent psychological research may lead to the prediction that tweets with generics about political groups are more common than tweets with generics about ethnic groups, we found the opposite. However, consistent with recent claims that political animosity is less constrained by social norms than animosity against gender and ethnic groups, negative tweets with generics about political groups were significantly more prevalent and retweeted than negative tweets about ethnic groups. Our study provides the first ML-based insights into the use and impact of social generics on Twitter. (shrink)

In real-time, Twitter strongly imprints world events, popular culture, and the day-to-day; Twitter records an ever growing compendium of language use and change; and Twitter has been shown to enable certain kinds of prediction. Vitally, and absent from many standard corpora such as books and news archives, Twitter also encodes popularity and spreading through retweets. Here, we describe Storywrangler, an ongoing, day-scale curation of over 100 billion tweets containing around 1 trillion 1-grams from 2008 to 2020. For each day, we (...) break tweets into 1-, 2-, and 3-grams across 150+ languages, record usage frequencies, and generate Zipf distributions. We make the data set available through an interactive time series viewer, and as downloadable time series and daily distributions. We showcase a few examples of the many possible avenues of study we aim to enable including how social amplification can be visualized through ‘contagiograms’. (shrink)

This article derives from data collected over a six-month period between February and August 2022. Its sampling pertains to members of two general Twitter Lists of philosophy professionals: “Philosophers on Twitter”, limited to a maximum of 4500 active accounts, and “Philosophers”, restricted to accounts surpassing 1000 followers and currently including over 1,100 individuals. The totality of members of these two Lists is referenced in this article as “Philosophy Twitter”. -/- Data was collected in five principal ways from members of these (...) two Lists: 1) Monitoring the List streams, 2) addressing members, including following, retweeting, liking, endorsing, asking, commenting, and replying, 3) probing members’ Twitter activities in their Profiles (“Tweets & replies”), 4) reviewing members’ Twitter Bios, CVs, professional profiles, and websites, and 5) network analysis of members’ quantitative and qualitative association and interaction profiles. -/- The study of this material aimed at revealing interpersonal social structures and processes of philosophy professionals by their Twitter conduct. Its personal purview focused on creators, teachers, researchers, and students and thus excepted schools, colleges, universities, formal associations, and publishers. Particular attention was given to gaining insights on substantive orientation, cooperation, and constructive dialogue versus hierarchic and tribal characteristics. (shrink)