Neutral bots probe political bias on social media

Chen, Wen; Pacheco, Diogo; Yang, Kai-Cheng; Menczer, Filippo

doi:10.1038/s41467-021-25738-6

Cited by 41 publications

(28 citation statements)

References 59 publications

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“…Action is needed especially because our audit found more biases among right-leaning users and our intervention showed that it takes considerably more effort to debias YouTube recommendations for these users as well (see also [113] for similar biases on Twitter, where right-leaning accounts did not experience exposure to moderate information and produced increasingly partisan content, partly due to bots and social networks). Although these right-wing biases are not very pronounced, they are there, detected, and visible.…”

Section: Discussionmentioning

confidence: 90%

YouTube, The Great Radicalizer? Auditing and Mitigating Ideological Biases in YouTube Recommendations

Haroon¹,

Chhabra²,

Liu³

et al. 2022

Preprint

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Recommendations algorithms of social media platforms are often criticized for placing users in "rabbit holes" of (increasingly) ideologically biased content. Despite these concerns, prior evidence on this algorithmic radicalization is inconsistent. Furthermore, prior work lacks systematic interventions that reduce the potential ideological bias in recommendation algorithms. We conduct a systematic audit of YouTube's recommendation system using a hundred thousand sock puppets to determine the presence of ideological bias (i.e., are recommendations aligned with users' ideology), its magnitude (i.e., are users recommended an increasing number of videos aligned with their ideology), and radicalization (i.e., are the recommendations progressively more extreme). Furthermore, we design and evaluate a bottomup intervention to minimize ideological bias in recommendations without relying on cooperation from YouTube. We find that YouTube's recommendations do direct users -especially right-leaning users -to ideologically biased and increasingly radical content on both homepages and in up-next recommendations. Our intervention effectively mitigates the observed bias, leading to more recommendations to ideologically neutral, diverse, and dissimilar content, yet debiasing is especially challenging for right-leaning users. Our systematic assessment shows that while YouTube recommendations lead to ideological bias, such bias can be mitigated through our intervention. * Additional information, including the accompanying source code and data, is available at: https://github.com/ucdavis-noyce/YouTube-Auditing-Mitigating-Bias 1 Naturally, most citizens do not inhabit insular communities online [7,8]. The small set that does, however, is socially consequential. Those users tend to be more partisan, more politically active, and have a disproportionate influence on the democratic process [9,10]

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Section: Discussionmentioning

confidence: 90%

YouTube, The Great Radicalizer? Auditing and Mitigating Ideological Biases in YouTube Recommendations

Haroon¹,

Chhabra²,

Liu³

et al. 2022

Preprint

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show abstract

“…In terms of content, malicious bots are found to engage other accounts with negative and inflammatory language [11] or hate speech [30,17]. In some cases, bots form dense social networks to boost engagement and popularity metrics and to amplify each other's messages [31,32,33].…”

Section: Introductionmentioning

confidence: 99%

Botometer 101: Social bot practicum for computational social scientists

Yang,

Ferrara,

Menczer

2022

Preprint

Self Cite

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Social bots have become an important component of online social media. Deceptive bots, in particular, can manipulate online discussions of important issues ranging from elections to public health, threatening the constructive exchange of information. Their ubiquity makes them an interesting research subject and requires researchers to properly handle them when conducting studies using social media data. Therefore it is important for researchers to gain access to bot detection tools that are reliable and easy to use. This paper aims to provide an introductory tutorial of Botometer, a public tool for bot detection on Twitter, for readers who are new to this topic and may not be familiar with programming and machine learning. We introduce how Botometer works, the different ways users can access it, and present a case study as a demonstration. Readers can use the case study code as a template for their own research. We also discuss recommended practice for using Botometer.

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“…For instance, on social media platforms, false information is typically shared by more users, and travels far more rapidly than reliable information [19]. This condition is further exacerbated by the presence of social bots [20,21] -i.e., automated accounts impersonating humans -that act as magnifiers of noise, conflicts and (mis)information spread [22][23][24][25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

The voice of few, the opinions of many: evidence of social biases in Twitter COVID-19 fake news sharing

Piergiorgio¹,

Andrighetto²,

Gallotti³

et al. 2021

Preprint

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Online platforms play a relevant role in the creation and diffusion of false or misleading news. Concerningly, the COVID-19 pandemic is shaping a communication network -barely considered in the literature -which reflects the emergence of collective attention towards a topic that rapidly gained universal interest. Here, we characterize the dynamics of this network on Twitter, analyzing how unreliable content distributes among its users. We find that a minority of accounts is responsible for the majority of the misinformation circulating online, and identify two categories of users: a few active ones, playing the role of "creators", and a majority playing the role of "consumers". The relative proportion of these groups (≈ 14% creators -86% consumers) appears stable over time: Consumers are mostly exposed to the opinions of a vocal minority of creators, that could be mistakenly understood as of representative of the majority of users. The corresponding pressure from a perceived majority is identified as a potential driver of the ongoing COVID-19 infodemic.

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Neutral bots probe political bias on social media

Cited by 41 publications

References 59 publications

YouTube, The Great Radicalizer? Auditing and Mitigating Ideological Biases in YouTube Recommendations

YouTube, The Great Radicalizer? Auditing and Mitigating Ideological Biases in YouTube Recommendations

Botometer 101: Social bot practicum for computational social scientists

The voice of few, the opinions of many: evidence of social biases in Twitter COVID-19 fake news sharing

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