The rise of social bots

Ferrara, Emilio; Varol, Onur; Davis, Clayton A.; Menczer, Filippo; Flammini, Alessandro

doi:10.1145/2818717

Cited by 1,647 publications

(1,321 citation statements)

References 31 publications

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“…A challenge for this type of research is how to test the efficacy of different types of corrections on large samples and possibly in the naturalistic settings of social media. Social bots, which so far have been responsible for spreading large amounts of misinformation [57][58][59][60], could be employed to this end, since initial evidence shows that they can be employed to spread positive messages [61].…”

Section: A Call To Action For Computational Social Scientistsmentioning

confidence: 99%

Fighting fake news: a role for computational social science in the fight against digital misinformation

Ciampaglia

2017

J Comput Soc Sc

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The massive, uncontrolled, and oftentimes systematic spread of inaccurate and misleading information on the Web and social media poses a major risk to society. Digital misinformation thrives on an assortment of cognitive, social, and algorithmic biases and current countermeasures based on journalistic corrections do not seem to scale up. By their very nature, computational social scientists could play a twofold role in the fight against fake news: first, they could elucidate the fundamental mechanisms that make us vulnerable to misinformation online and second, they could devise effective strategies to counteract misinformation.

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Section: A Call To Action For Computational Social Scientistsmentioning

confidence: 99%

Fighting fake news: a role for computational social science in the fight against digital misinformation

Ciampaglia

2017

J Comput Soc Sc

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“…They designed Host Botnet Detection Software (HBD's) to detect Zeus botnet in user's computers. Ferrara et al 44 discuss the characteristics of modern, sophisticated socialbots. They also discuss current efforts aimed at detection of socialbots in Twitter.…”

Section: Socialbot Detectionmentioning

confidence: 99%

Understanding socialbot behavior on end hosts

Cao

et al. 2017

International Journal of Distributed Sensor Networks

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Server-side socialbot detection approaches can identify malicious accounts and spams in online social networks. However, they cannot detect socialbot processes, residing on user hosts, which control these accounts. Therefore, new approaches are needed to detect socialbots on hosts. The fundamental to design host-side detecting approaches is to gain an insight into the behaviors of socialbots on host. In this article, we analyzed a series of representative socialbots in depth and summarized the typical features of socialbot behaviors. We proposed a new approach to defense against socialbots on end host. The contributions of this article are threefold: (1) our analysis approach can be used for reference during analyzing new socialbots in the future; (2) we provide several behavior features of socialbots on hosts, including network flow through which socialbots communicate with botmasters through the online social network, system calls via which socialbots conduct an activity, and process information of socialbots running on hosts. These features can be used by someone to design approaches to identifying socialbots on a host; (3) our proposed detection approach can effectively distinguish between a socialbot and a benign application on end hosts.

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“…e.g. Ferrara et al (2016)) and industry (Gartner, 2016). While there are many related reasons for this development, we think that three key changes were particularly important:…”

Section: Introductionmentioning

confidence: 99%

Evaluating Natural Language Understanding Services for Conversational Question Answering Systems

Braun¹,

Hernandez-Mendez²,

Matthes³

et al. 2017

Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue

167

125

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Conversational interfaces recently gained a lot of attention. One of the reasons for the current hype is the fact that chatbots (one particularly popular form of conversational interfaces) nowadays can be created without any programming knowledge, thanks to different toolkits and socalled Natural Language Understanding (NLU) services. While these NLU services are already widely used in both, industry and science, so far, they have not been analysed systematically. In this paper, we present a method to evaluate the classification performance of NLU services. Moreover, we present two new corpora, one consisting of annotated questions and one consisting of annotated questions with the corresponding answers. Based on these corpora, we conduct an evaluation of some of the most popular NLU services. Thereby we want to enable both, researchers and companies to make more educated decisions about which service they should use.

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The rise of social bots

Abstract: Today's social bots are sophisticated and sometimes menacing. Indeed, their presence can endanger online ecosystems as well as our society.

Cited by 1,647 publications

References 31 publications

Fighting fake news: a role for computational social science in the fight against digital misinformation

Fighting fake news: a role for computational social science in the fight against digital misinformation

Understanding socialbot behavior on end hosts

Evaluating Natural Language Understanding Services for Conversational Question Answering Systems

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