Political Bots and the Manipulation of Public Opinion in Venezuela

Forelle, Michelle C.; Howard, Phil; Monroy-Hernández, Andrés; Savage, Saiph

doi:10.48550/arxiv.1507.07109

Cited by 11 publications

(17 citation statements)

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“…A direct strategy was the most effective for getting relevant participation. We believe that the growing number of persuasive online agents [10,21] has made people prefer interactions with greater transparency and a less "manipualtive" approach. Nevertheless, our experiments highlight the real-world feasibility of using online bots to call volunteers to action.…”

Section: Discussionmentioning

confidence: 99%

“…We designed Botivist to behave in line with other mainstream conversational agents. Most previous work on online bots has focused more on developing techniques to detect these agents [10]. However, this "Turing test" approach has left out the possibility that bots can persuade and mobilize people on social media.…”

Section: Botivist: Online Automated Agentsmentioning

confidence: 99%

“…The proliferation of persuasive technology in social media [10] may effect how people react to certain strategies, perhaps driving a preference for less "manipulative" tactics. On the other hand, the fact that simply changing a bot's message changed subsequent response rates, without needing any interface modification or design of complex bot behavior, suggests that designing adequate dialog for online automated agents can have a strong effect on increasing volunteer participation in activism.…”

Section: Feasibilitymentioning

confidence: 99%

“…Companies, organizations, and even governments have taken to social media to reach a wider audience and influence behavior [5]. Some have gone as far as to set up fake accounts operated by automated software (bots) in order to feign support and influence real users [10]. In Mexico's 2012 presidential election, the winning party used over 10,000 bots to swamp online discussion [21].…”

Section: Introductionmentioning

confidence: 99%

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Botivist

Savage

Monroy-Hernández

Höllerer

2016

Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work &Amp; Social Computing

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To help activists call new volunteers to action, we present Botivist: a platform that uses Twitter bots to find potential volunteers and request contributions. By leveraging different Twitter accounts, Botivist employs different strategies to encourage participation. We explore how people respond to bots calling them to action using a test case about corruption in Latin America. Our results show that the majority of volunteers (> 80%) who responded to Botivist's calls to action contributed relevant proposals to address the assigned social problem. The number of contributions and their relevance varied by the strategy used. Some strategies that work well offline and face-to-face appeared to hinder people's participation when used by an online bot. We analyze user behavior in response to being approached by bots with an activist purpose. We also provide strong evidence for the value of this type of civic media, and derive design implications.

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Section: Discussionmentioning

confidence: 99%

Section: Botivist: Online Automated Agentsmentioning

confidence: 99%

Section: Feasibilitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Botivist

Savage

Monroy-Hernández

Höllerer

2016

Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work &Amp; Social Computing

Self Cite

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“…In this section we study the feasibility of predicting pump and dump events from the social media and market data only, without relying on the availability of Telegram messages 5 . Specifically, we focus on the following two classification tasks:…”

Section: Pump Attempts Predictionmentioning

confidence: 99%

Identifying and Analyzing Cryptocurrency Manipulations in Social Media

Mirtaheri

Abu-El-Haija²,

Steeg³

et al. 2019

Preprint

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Interest surrounding cryptocurrencies, digital or virtual currencies that are used as a medium for financial transactions, has grown tremendously in recent years. The anonymity surrounding these currencies makes investors particularly susceptible to fraud-such as "pump and dump" scams-where the goal is to artificially inflate the perceived worth of a currency, luring victims into investing before the scammers can sell their holdings. Because of the speed and relative anonymity offered by social platforms such as Twitter and Telegram, social media has become a preferred platform for scammers who wish to spread false hype about the cryptocurrency they are trying to pump. In this work we propose and evaluate a computational approach that can automatically identify pump and dump scams as they unfold by combining information across social media platforms. We also develop a multi-modal approach for predicting whether a particular pump attempt will succeed or not. Finally, we analyze the prevalence of bots in cryptocurrency related tweets, and observe a significant significant presence of bots during the pump attempts. KEYWORDScryptocurrency, pump and dump, social media data mining, anomaly detection

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