An Empirical Analysis of Human-Bot Interaction on Reddit

Ma, Ming-Cheng; Lalor, John P.

doi:10.18653/v1/2020.wnut-1.14

Cited by 8 publications

(5 citation statements)

References 15 publications

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“…One botnet of roughly 13,000 bot accounts was observed tweeting about Brexit, with most of these bot accounts disappearing from Twitter shortly after the vote [ 24 ]. Bots of all types are ubiquitous on social media and have been studied on Reddit [ 25 , 26 ], Facebook [ 27 ], YouTube [ 28 ], and Twitter [ 29 ], among other platforms.…”

Section: What Are Bots?mentioning

confidence: 99%

Bots and Misinformation Spread on Social Media: Implications for COVID-19

Himelein-Wachowiak¹,

Giorgi²,

Devoto³

et al. 2021

J Med Internet Res

133

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As of March 2021, the SARS-CoV-2 virus has been responsible for over 115 million cases of COVID-19 worldwide, resulting in over 2.5 million deaths. As the virus spread exponentially, so did its media coverage, resulting in a proliferation of conflicting information on social media platforms—a so-called “infodemic.” In this viewpoint, we survey past literature investigating the role of automated accounts, or “bots,” in spreading such misinformation, drawing connections to the COVID-19 pandemic. We also review strategies used by bots to spread (mis)information and examine the potential origins of bots. We conclude by conducting and presenting a secondary analysis of data sets of known bots in which we find that up to 66% of bots are discussing COVID-19. The proliferation of COVID-19 (mis)information by bots, coupled with human susceptibility to believing and sharing misinformation, may well impact the course of the pandemic.

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Section: What Are Bots?mentioning

confidence: 99%

Bots and Misinformation Spread on Social Media: Implications for COVID-19

Himelein-Wachowiak¹,

Giorgi²,

Devoto³

et al. 2021

J Med Internet Res

133

View full text Add to dashboard Cite

show abstract

“…They find that sentiment of bot has a positive effect on the sentiment of the humans response. Furthermore, Ma and Lalor (2020) show that human responses tend to overlap with the bots original post. Compared to prior work studying accommodation-related aspects of human-bot interactions, this paper differs in two ways.…”

Section: Analyzing Human-bot Conversationsmentioning

confidence: 99%

“…Moreover, users tend to have positive reactions towards systems that code-switch. Ma and Lalor (2020) measure lexical entrainment between a specific Reddit bot and users. They find that sentiment of bot has a positive effect on the sentiment of the humans response.…”

Section: Analyzing Human-bot Conversationsmentioning

confidence: 99%

Detecting Bot-Generated Text by Characterizing Linguistic Accommodation in Human-Bot Interactions

Bhatt¹,

Rios²

2021

Preprint

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Language generation models' democratization benefits many domains, from answering health-related questions to enhancing education by providing AI-driven tutoring services. However, language generation models' democratization also makes it easier to generate human-like text at-scale for nefarious activities, from spreading misinformation to targeting specific groups with hate speech. Thus, it is essential to understand how people interact with bots and develop methods to detect bot-generated text. This paper shows that bot-generated text detection methods are more robust across datasets and models if we use information about how people respond to it rather than using the bot's text directly.We also analyze linguistic alignment, providing insight into differences between humanhuman and human-bot conversations.

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“…One botnet of roughly 13,000 bot accounts was observed tweeting about Brexit, with most of these bot accounts disappearing from Twitter shortly after the vote [24]. Bots of all types are ubiquitous on social media and have been studied on Reddit [25,26], Facebook [27], YouTube [28], and Twitter [29], among other platforms.…”

Section: Definition and Identificationmentioning

confidence: 99%

Bots and Misinformation Spread on Social Media: Implications for COVID-19 (Preprint)

Himelein-Wachowiak¹,

Giorgi²,

Devoto³

et al. 2021

Preprint

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UNSTRUCTURED As of December 2020, the SARS-CoV-2 virus has been responsible for over 78 million cases of COVID-19 worldwide, resulting in over 1.7 million deaths. In the United States in particular, protective measures against the COVID-19 pandemic have been hampered by political polarization and discrepancies among federal, state, and local policies. As a result, a huge amount of information surrounding COVID-19, some of it contradictory or blatantly false, has proliferated on social media. In this mixed scoping review, we survey the role of automated accounts, or “bots,” in spreading misinformation during past epidemics, natural disasters, and politically polarizing events through the lens of the COVID-19 pandemic. We also review strategies used by bots to spread (mis)information and machine learning methods for detecting bot activity. We conclude by conducting and presenting a secondary analysis of known bots, finding that up to 66% of bots are discussing COVID-19. The proliferation of COVID-19 (mis)information by bots, coupled with human susceptibility to believing and sharing misinformation, may well impact the course of the pandemic.

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An Empirical Analysis of Human-Bot Interaction on Reddit

Cited by 8 publications

References 15 publications

Bots and Misinformation Spread on Social Media: Implications for COVID-19

Bots and Misinformation Spread on Social Media: Implications for COVID-19

Detecting Bot-Generated Text by Characterizing Linguistic Accommodation in Human-Bot Interactions

Bots and Misinformation Spread on Social Media: Implications for COVID-19 (Preprint)

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