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Mazza, Michele; Cresci, Stefano; Avvenuti, Marco; Quattrociocchi, Walter; Tesconi, Maurizio

doi:10.1145/3292522.3326015

Cited by 119 publications

(40 citation statements)

References 38 publications

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“…Between 53% (96/182) and 66% (515/780) of these bots are actively tweeting about COVID-19. [50] a Original sample size is the number of bot IDs publicly released on the Bot Repository, while current sample size is the number of active accounts tweeting in 2020. Percentage discussing COVID-19 is the percentage of bots with at least one tweet containing a COVID-19 keyword out of those active in 2020.…”

Section: Are Bots Tweeting About Covid-19?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: Are Bots Tweeting About Covid-19?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

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“…Recent approaches in computer science turn towards unsupervised machine learning and group-based detection methods for the same reasons, e.g. by searching for similar temporal retweeting activities of otherwise unconnected accounts (Mazza et al, 2019), by detecting cross-user activity correlations (Chavoshi et al, 2016) or by comparing accounts' 'digital DNA sequences' in order to find groups with high behavioural similarities (Cresci et al, 2017a). These studies all report yielding better results than per-user methods that classify individual accounts.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

Bot, or not? Comparing three methods for detecting social bots in five political discourses

et al. 2021

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Social bots – partially or fully automated accounts on social media platforms – have not only been widely discussed, but have also entered political, media and research agendas. However, bot detection is not an exact science. Quantitative estimates of bot prevalence vary considerably and comparative research is rare. We show that findings on the prevalence and activity of bots on Twitter depend strongly on the methods used to identify automated accounts. We search for bots in political discourses on Twitter, using three different bot detection methods: Botometer, Tweetbotornot and “heavy automation”. We drew a sample of 122,884 unique user Twitter accounts that had produced 263,821 tweets contributing to five political discourses in five Western democracies. While all three bot detection methods classified accounts as bots in all our cases, the comparison shows that the three approaches produce very different results. We discuss why neither manual validation nor triangulation resolves the basic problems, and conclude that social scientists studying the influence of social bots on (political) communication and discourse dynamics should be careful with easy-to-use methods, and consider interdisciplinary research.

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“…They are later on concatenated with the rest of the metadata to build a potential input vector on top of a dense network. The model achieved a lower F1-score than the previous LSTM autoencoder performance [48]. However, certainly, the Bot-DenseNet model had a unique capability for analysing text features from multiple languages and due to its low-dimensional representation, was suitable for use in any application within the information retrieval (IR) framework.…”

Section: Deep Learning Methods For Social Media Bot Detection: Evalua...mentioning

confidence: 99%

Social media bot detection with deep learning methods: a systematic review

Hayawi

Saha²,

Masud

et al. 2023

Neural Comput & Applic

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Social bots are automated social media accounts governed by software and controlled by humans at the backend. Some bots have good purposes, such as automatically posting information about news and even to provide help during emergencies. Nevertheless, bots have also been used for malicious purposes, such as for posting fake news or rumour spreading or manipulating political campaigns. There are existing mechanisms that allow for detection and removal of malicious bots automatically. However, the bot landscape changes as the bot creators use more sophisticated methods to avoid being detected. Therefore, new mechanisms for discerning between legitimate and bot accounts are much needed. Over the past few years, a few review studies contributed to the social media bot detection research by presenting a comprehensive survey on various detection methods including cutting-edge solutions like machine learning (ML)/deep learning (DL) techniques. This paper, to the best of our knowledge, is the first one to only highlight the DL techniques and compare the motivation/effectiveness of these techniques among themselves and over other methods, especially the traditional ML ones. We present here a refined taxonomy of the features used in DL studies and details about the associated pre-processing strategies required to make suitable training data for a DL model. We summarize the gaps addressed by the review papers that mentioned about DL/ML studies to provide future directions in this field. Overall, DL techniques turn out to be computation and time efficient techniques for social bot detection with better or compatible performance as traditional ML techniques.

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Cited by 119 publications

References 38 publications

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

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

Bot, or not? Comparing three methods for detecting social bots in five political discourses

Social media bot detection with deep learning methods: a systematic review

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