You talkin' to me? Exploring Human/Bot Communication Patterns during Riot Events. Issue 1 (January 2020)

• Record Type: Journal Article
• Authors: Kušen, Ema; Strembeck, Mark
• Is Part Of: Information processing & management. Volume 57:Issue 1(2020:Jan.)
• Publication Date: 2020-01
• Subjects: Emotion analysis -- Emotion-exchange motif -- Network motif -- Network analysis -- Riot -- Social bot -- Twitter; Information storage and retrieval systems -- Periodicals; Information science -- Periodicals; Systèmes d'information -- Périodiques; Sciences de l'information -- Périodiques; Information science; Information storage and retrieval systems; Periodicals; 658.4038
• DOI: 10.1016/j.ipm.2019.102126
• Languages: English
• ISSNs: 0306-4573

Abstract

Highlights: Human-human and human-bot conversation on Twitter can be characterized by specific patterns that emerge as bot and human accounts exchange emotion-conveying messages. These patters come in form of statistically-significant subgraphs that we call e motion-exchange motifs, which are an extension of the traditional network motifs. There are distinct emotion-exchange motifs that are characteristic for a human-like communication. These motifs include self-loops, reciprocal edges, and transitive triads. Human users tend to use self-loops when communicating anger, a mechanism used to bypass the 140-character restriction on Twitter. As they communicate with humans, bot accounts form only a smaller subset of emotion-exchange motifs that, unlike the ones found in a human-human communication, involve only one-way edges (message chain motifs, broadcasting motifs, and a message-receiver motifs without reciprocal edges). Specific to the events considered in this study (riot events), bots were responsible for a dissemination of messages conveying f ear. These messages receives a considerable high number of retweets in our data-sets. The use of fear is also evident in the fear-exchange motifs where bots consistently take over a message-sender role (whereas a human is consistently a message receiver). Abstract: We analyze a data-set including more than 4.5 million tweets related to four highly emotional riot events. In particular, we examine statistically significant structural patterns that emerge as humans directly engage in an exchange of emotional messages with other humans on Twitter. Furthermore, we compare typical human-to-human communication patterns with those that emerge as bots engage in an emotional message-exchange with human users. To this end, we apply the novel concept of emotion-exchange motifs . We found that a) human-to-human conversations results in a variety of motifs that contain reciprocal edges and self-loops, b) bots predominantly contribute to the emergence of message broadcasting, single-way message sending behavior, c) in contrast to previous findings we found that in certain events bots frequently engage in direct message exchanges with humans, d) during riot events bots tend to direct fear -conveying messages to human users.