The strength of weak bots

Keijzer, Marijn A.; Mäs, Michael

doi:10.1016/j.osnem.2020.100106

Cited by 17 publications

(12 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While previous work has already investigated the effects of bots on social networks (Cheng et al, 2020; Keijzer and Mäs, 2021; Ross et al, 2019), in this article we offer a psychological perspective on how deceitful and manipulative social bots and social media users engage with each other through following, retweeting, quoted retweeting, and commenting on Twitter. We argue that investigating such engagement with social bots is crucial to better understand how social bot communication affects not only social media networks but also the user’s contribution to the amplification of malicious social bot communication.…”

Section: Introductionmentioning

confidence: 99%

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

Wischnewski

Ngo

Bernemann

et al. 2022

New Media & Society

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

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

Wischnewski

Ngo

Bernemann

et al. 2022

New Media & Society

View full text Add to dashboard Cite

show abstract

“…Contrary to previous studies investigating social media bots, our work does not model direct interactions between bots and human agents (arguably representing a minority of interactions) but focuses on indirect effects via recommendation systems. Agents-based simulations have shown how bots can have a long-range, pervasive, and most critically stealthy influence on the network even without direct social influence (Keijzer and Mäs 2021). Our findings highlight that malicious agents, such as bots and trolls factories, can further increase their influence by infiltrating the internal representations of trained models tasked with content filtering.…”

Section: Discussionmentioning

confidence: 71%

“…We call this type of influence machine-mediated indirect influence, as opposed to indirect influence occurring via intermediary nodes (a bot may directly influence one human but indirectly influence all the humans to whom the first human is connected). Recent research in opinion dynamics has already shown the importance of weak ties and the indirect influence of bots on the rest of the network (Keijzer and Mäs 2021;Aldayel and Magdy 2022). Here, however, we are especially interested in the influence of social bots on network opinion dynamics when platform-wide algorithmic content recommendation mediates information sharing.…”

mentioning

confidence: 99%

Bots influence opinion dynamics without direct human-bot interaction: the mediating role of recommender systems

2022

View full text Add to dashboard Cite

Bots’ ability to influence public discourse is difficult to estimate. Recent studies found that hyperpartisan bots are unlikely to influence public opinion because bots often interact with already highly polarized users. However, previous studies focused on direct human-bot interactions (e.g., retweets, at-mentions, and likes). The present study suggests that political bots, zealots, and trolls may indirectly affect people’s views via a platform’s content recommendation system's mediating role, thus influencing opinions without direct human-bot interaction. Using an agent-based opinion dynamics simulation, we isolated the effect of a single bot—representing 1% of nodes in a network—on the opinion of rational Bayesian agents when a simple recommendation system mediates the agents’ content consumption. We compare this experimental condition with an identical baseline condition where such a bot is absent. Across conditions, we use the same random seed and a psychologically realistic Bayesian opinion update rule so that conditions remain identical except for the bot presence. Results show that, even with limited direct interactions, the mere presence of the bot is sufficient to shift the average population’s opinion. Virtually all nodes—not only nodes directly interacting with the bot—shifted towards more extreme opinions. Furthermore, the mere bot’s presence significantly affected the internal representation of the recommender system. Overall, these findings offer a proof of concept that bots and hyperpartisan accounts can influence population opinions not only by directly interacting with humans but also by secondary effects, such as shifting platforms’ recommendation engines’ internal representations. The mediating role of recommender systems creates indirect causal pathways of algorithmic opinion manipulation.

show abstract

“…Mäs and Karlsruhe sociologist Marijn Keijzer found essentially the same result earlier this year in a model of online bots (21). "Bots that have many followers and that are very aggressively posting falsehoods," says Mäs, "are less effective than bots that are not having many followers and only from time to time spread their content.…”

Section: Antisocial Mediamentioning

confidence: 77%

Modeling the power of polarization

Waldrop¹

2021

Proc. Natl. Acad. Sci. U.S.A.

View full text Add to dashboard Cite

In 2016, when Vicky Chuqiao Yang first started to work on computer simulations of US politics, she was fascinated by the realization that the left-right standoff widely described as "polarization" is not one thing. "There are two kinds of polarization that the media and the public often get confused," says Yang, an applied mathematician at the Santa Fe Institute in New Mexico. One type is issue polarization: "how much people disagree on policies like 'What should be the tax rates?', or 'What should be the laws to regulate guns?'."Those divisions have been widening of late. But they aren't nearly as incendiary as social or "affective" polarization, which is about anger, distrust, resentment, tribal identity, and mutual loathing (see Fig. 1). As a team of prominent social scientists warned last year, social polarization in conjunction with legislative gridlock and hyper-partisan media have created an "American sectarianism" that threatens democracy itself (1).Researchers are trying to understand why social polarization is on the rise and-perhaps more importantly-what we can do about it. Can we find solutions by focusing on racial anxieties, conspiracy theories, and social media echo chambers that endlessly reinforce a single viewpoint? Or do we also need to look for more fundamental forces at work? Americans have always been polarized in some ways. But using models, a cadre of researchers is trying to understand why social polarization is on the rise and-perhaps more importantly-what we can do about it. Image credit: Dave Cutler (artist).

show abstract

The strength of weak bots

Cited by 17 publications

References 29 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

Bots influence opinion dynamics without direct human-bot interaction: the mediating role of recommender systems

Modeling the power of polarization

Contact Info

Product

Resources

About