Do Social Bots (Still) Act Different to Humans? – Comparing Metrics of Social Bots with Those of Humans

Stieglitz, Stefan; Brachten, Florian; Berthelé, Davina; Schlaus, Mira; Venetopoulou, Chrissoula; Veutgen, Daniel

doi:10.1007/978-3-319-58559-8_30

Cited by 41 publications

(37 citation statements)

References 31 publications

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“…This example illustrates that conspicuous repetition patterns are not always due to the activity of social or political bots but can also reflect the highly habitualized or intuitive user behavior that Kitada has described as connective sociality and that has dramatically changed the frequency and the way in which users engage with each other. Stieglitz et al 44 arrive at the conclusion that one can (still) differentiate between bots and human users based on their metrics (number of followers, Retweets, and used links per day). However, one might argue that in terms of their behavior, typical users exhibit a very narrow activity on social media with their rigid structure of possible usages (posting, liking, sharing), which is not too different from the calculable or programmed behavior of a social bot, exploiting the narrowness of these options.…”

Section: Sampling and Method: Detecting Social Bots In The 2014 Genermentioning

confidence: 99%

Japan's 2014 General Election: Political Bots, Right-Wing Internet Activism, and Prime Minister Shinzō Abe's Hidden Nationalist Agenda

2017

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In this article, we present results on the identification and behavioral analysis of social bots in a sample of 542,584 Tweets, collected before and after Japan's 2014 general election. Typical forms of bot activity include massive Retweeting and repeated posting of (nearly) the same message, sometimes used in combination. We focus on the second method and present (1) a case study on several patterns of bot activity, (2) methodological considerations on the automatic identification of such patterns and the prerequisite near-duplicate detection, and (3) we give qualitative insights into the purposes behind the usage of social/political bots. We argue that it was in the latency of the semi-public sphere of social media—and not in the visible or manifest public sphere (official campaign platform, mass media)—where Shinzō Abe's hidden nationalist agenda interlocked and overlapped with the one propagated by organizations such as Nippon Kaigi and Internet right-wingers (netto uyo) during the election campaign, the latter potentially forming an enormous online support army of Abe's agenda.

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Section: Sampling and Method: Detecting Social Bots In The 2014 Genermentioning

confidence: 99%

Japan's 2014 General Election: Political Bots, Right-Wing Internet Activism, and Prime Minister Shinzō Abe's Hidden Nationalist Agenda

2017

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“…A social bot is generally understood as a program “that automatically produces content and interacts with humans on social media” (Ferrara et al, , p. 96). As Stieglitz, Brachten, Ross, and Jung () note in a comprehensive literature review of social bots, this definition often includes a stipulation that social bots mimic human users. For example, Abokhodair, Yoo, and McDonald (, p. 840) define social bots as “automated social agents” that are public facing and that seem to act in ways that are not dissimilar to how a real human may act in an online space.…”

Section: What Are Bots? Building a Bot Typologymentioning

confidence: 99%

Unpacking the Social Media Bot: A Typology to Guide Research and Policy

Gorwa¹,

Guilbeault²

2018

Policy & Internet

155

100

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Amidst widespread reports of digital influence operations during major elections, policymakers, scholars, and journalists have become increasingly interested in the political impact of social media 'bots.' Most recently, platform companies like Facebook and Twitter have been summoned to testify about bots as part of investigations into digitally-enabled foreign manipulation during the 2016 US Presidential election. Facing mounting pressure from both the public and from legislators, these companies have been instructed to crack down on apparently malicious bot accounts. But as this article demonstrates, since the earliest writings on bots in the 1990s, there has been substantial confusion as to exactly what a 'bot' is and what exactly a bot does. We argue that multiple forms of ambiguity are responsible for much of the complexity underlying contemporary bot-related policy, and that before successful policy interventions can be formulated, a more comprehensive understanding of bots -especially how they are defined and measured -will be needed. In this article, we provide a history and typology of different types of bots, provide clear guidelines to better categorize political automation and unpack the impact that it can have on contemporary technology policy, and outline the main challenges and ambiguities that will face both researchers and legislators concerned with bots in the future.

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“…We noted every suspicious behavior exhibited by these highly-suspected bot accounts with respect to account profile information, friends, followers, interaction with other accounts, tweet content, and posting behavior. We also familiarized ourselves with bot characteristics and behaviors reported in previous studies [1,8,14,25,53,58]. Following this, we have created a list of bot characteristics described in Table 2.…”

Section: Ground Truthmentioning

confidence: 99%

Hateful People or Hateful Bots?

Albadi

Kurdi

Mishra

2019

Proc. ACM Hum.-Comput. Interact.

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Arabic Twitter space is crawling with bots that fuel political feuds, spread misinformation, and proliferate sectarian rhetoric. While efforts have long existed to analyze and detect English bots, Arabic bot detection and characterization remains largely understudied. In this work, we contribute new insights into the role of bots in spreading religious hatred on Arabic Twitter and introduce a novel regression model that can accurately identify Arabic language bots. Our assessment shows that existing tools that are highly accurate in detecting English bots don't perform as well on Arabic bots. We identify the possible reasons for this poor performance, perform a thorough analysis of linguistic, content, behavioral and network features, and report on the most informative features that distinguish Arabic bots from humans as well as the differences between Arabic and English bots. Our results mark an important step toward understanding the behavior of malicious bots on Arabic Twitter and pave the way for a more effective Arabic bot detection tools.

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Do Social Bots (Still) Act Different to Humans? – Comparing Metrics of Social Bots with Those of Humans

Cited by 41 publications

References 31 publications

Japan's 2014 General Election: Political Bots, Right-Wing Internet Activism, and Prime Minister Shinzō Abe's Hidden Nationalist Agenda

Japan's 2014 General Election: Political Bots, Right-Wing Internet Activism, and Prime Minister Shinzō Abe's Hidden Nationalist Agenda

Unpacking the Social Media Bot: A Typology to Guide Research and Policy

Hateful People or Hateful Bots?

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