Characterizing the role of bots' in polarized stance on social media
Bibliographic Data
| ID | 4658710 |
|---|---|
| Authors | Abeer Aldayel (0000-0002-0843-0123, University of Edinburgh, corresponding author), Walid Magdy (0000-0001-9676-1338, University of Edinburgh) |
| Year | 2022 |
| Volume | 12 |
| Issue | 1 |
| Pages | 30-30 |
| Publication date | 2022-12-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Social Network Analysis and Mining (JOURNAL) |
| Journal identifiers | ISSN: 1869-5450 • E-ISSN: 1869-5469 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1007/s13278-022-00858-z |
| PMID | 35136453 |
| OpenAlex | W4210451622 |
| Language | EN |
| Citations received | 12 |
| References cited | 50 |
There is a rising concern with social bots that imitate humans and manipulate opinions on social media. Current studies on assessing the overall effect of bots on social media users mainly focus on evaluating the diffusion of discussions on social networks by bots. Yet, these studies do not confirm the relationship between bots and users' stances. This study fills in the gap by analyzing if these bots are part of the signals that formulated social media users' stances towards controversial topics. We analyze users' online interactions that are predictive to their stances and identify the bots within these interactions. We applied our analysis on a dataset of more than 4000 Twitter users who expressed a stance on seven different topics. We analyzed those users' direct interactions and indirect exposures with more than 19 million accounts. We identify the bot accounts for supporting/against stances, and compare them to other types of accounts, such as the accounts of influential and famous users. Our analysis showed that bot interactions with users who had specific stances were minimal when compared to the influential accounts. Nevertheless, we found that the presence of bots was still connected to users' stances, especially in an indirect manner, as users are exposed to the content of the bots they follow, rather than by directly interacting with them by retweeting, mentioning, or replying
Data science · Internet privacy · Social media · World Wide Web · Computer Science · Misinformation and Its Impacts · Opinion Dynamics and Social Influence · Spam and Phishing Detection
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When Corrections Fail
| Unique citing works | 12 |
|---|---|
| Citations per year | 3 |
| Citation span | 2022 - 2026 (5) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 12 |