Which Users Reply to and Interact with Twitter Social Bots?

Wald, Randall; Khoshgoftaar, Taghi M.; Napolitano, Amri; Sumner, Chris

doi:10.1109/ictai.2013.30

Cited by 13 publications

(6 citation statements)

References 20 publications

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“…Trying to identify which users are more susceptible to engage with social bots, Wagner et al (2012), for example, found that more interactive users were more likely to reply to and befriend social bots. Similarly, Wald et al (2013) found that users' Klout score 1 and the total number of followers predicted the likelihood of users replying to or following a social bot. Results by Cardoso et al (2019) were especially alarming.…”

Section: Theoretical Backgroundmentioning

confidence: 88%

“I agree with you, bot!” How users (dis)engage with social bots on Twitter

Wischnewski

Ngo

Bernemann

et al. 2022

New Media & Society

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This article investigates under which conditions users on Twitter engage with or react to social bots. Based on insights from human–computer interaction and motivated reasoning, we hypothesize that (1) users are more likely to engage with human-like social bot accounts and (2) users are more likely to engage with social bots which promote content congruent to the user’s partisanship. In a preregistered 3 × 2 within-subject experiment, we asked N = 223 US Americans to indicate whether they would engage with or react to different Twitter accounts. Accounts systematically varied in their displayed humanness (low humanness, medium humanness, and high humanness) and partisanship (congruent and incongruent). In line with our hypotheses, we found that the more human-like accounts are, the greater is the likelihood that users would engage with or react to them. However, this was only true for accounts that shared the same partisanship as the user.

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Section: Theoretical Backgroundmentioning

confidence: 88%

“I agree with you, bot!” How users (dis)engage with social bots on Twitter

Wischnewski

Ngo

Bernemann

et al. 2022

New Media & Society

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“…Their results indicate that users more "open" to social interactions are more susceptible to attacks. Subsequently, a similar study by Wald et al [42] found that the Klout score, number of followers and friends, are good predictors of whether a user will interact with bots.…”

Section: Bots In Osnsmentioning

confidence: 89%

Reverse Engineering Socialbot Infiltration Strategies in Twitter

Freitas

Benevenuto

Ghosh

et al. 2015

Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015

106

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Data extracted from social networks like Twitter are increasingly being used to build applications and services that mine and summarize public reactions to events, such as traffic monitoring platforms, identification of epidemic outbreaks, and public perception about people and brands. However, such services are vulnerable to attacks from socialbots − automated accounts that mimic real users − seeking to tamper statistics by posting messages generated automatically and interacting with legitimate users. Potentially, if created in large scale, socialbots could be used to bias or even invalidate many existing services, by infiltrating the social networks and acquiring trust of other users with time. This study aims at understanding infiltration strategies of socialbots in the Twitter microblogging platform. To this end, we create 120 socialbot accounts with different characteristics and strategies (e.g., gender specified in the profile, how active they are, the method used to generate their tweets, and the group of users they interact with), and investigate the extent to which these bots are able to infiltrate the Twitter social network. Our results show that even socialbots employing simple automated mechanisms are able to successfully infiltrate the network. Additionally, using a 2 k factorial design, we quantify infiltration effectiveness of different bot strategies. Our analysis unveils findings that are key for the design of detection and counter measurements approaches.

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“…Twitter's following function can help users receive public posts from targeted users. The number of 'follower' refers to how many people are 'following' an account (Wald, et al, 2013). Previous studies found that social media bots had a higher number of 'following' and fewer 'followers' than human users (Stieglitz, et al, 2017).…”

Section: Basic Statisticsmentioning

confidence: 99%

Examining the differences between human and bot social media accounts: A case study of the Russia-Ukraine War

Shen

Zhang²,

Zhang³

et al. 2023

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In this study, we examined online conversations on Twitter about the Russia-Ukraine War and investigated differences between bots and non-bots accounts. Using ‘Russia’ and ‘Ukraine’ as keywords, we employed a Twitter API to collect data from 17 February to 18 March on Twitter. We obtained a large dataset of over 3.7 million tweets generated by about one million distinct accounts. We then analyzed one percent of the data using interval sampling for bot detection and found that about 13.4 percent of the accounts were social media bots, responsible for about 16.7 percent of the tweets. We examined the difference between bots and non-bots regarding online conversations on the Russia-Ukraine War through account analysis, textual analysis, and interaction analysis. The results show that bots exist on both sides, bots from the Ukrainian side contributed a louder voice while bots on the Russian side demonstrated more effective communication. In addition, there were differences and similarities between bots and non-bots in the behavior of online conversations, but the difference seemed to be relatively weaker than those found in previous studies.

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Which Users Reply to and Interact with Twitter Social Bots?

Cited by 13 publications

References 20 publications

“I agree with you, bot!” How users (dis)engage with social bots on Twitter

“I agree with you, bot!” How users (dis)engage with social bots on Twitter

Reverse Engineering Socialbot Infiltration Strategies in Twitter

Examining the differences between human and bot social media accounts: A case study of the Russia-Ukraine War

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