Spotting Political Social Bots in Twitter: A Use Case of the 2019 Spanish General Election

Pastor-Galindo, Javier; Zago, Mattia; Nespoli, Pantaleone; Bernal, Sergio López; Celdrán, Alberto Huertas; Pérez, Manuel Gil; Ruipérez‐Valiente, José A.; Pérez, Gregorio Martínez; Mármol, Félix Gómez

doi:10.1109/tnsm.2020.3031573

Cited by 41 publications

(22 citation statements)

References 32 publications

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“…It is not just elections that have come to their attention, but also local conflict situations, such as the strike in 2017 or the US impeachment debate and the Catalan referendum. The increasing popularity of the use of fake accounts in political campaigns is clearly demonstrated in the work of Pastor-Galindo and colleagues [31]. Whereas in 2014 they noted artificial activity in two countries, in 2019 they noted 13 countries where fake Twitter activity was used during election campaigns.…”

Section: Related Workmentioning

confidence: 96%

Detection of Induced Activity in Social Networks: Model and Methodology

2021

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This paper examines the problem of social media special operations and especially induced support in social media during political election campaigns. The theoretical background of the paper is based on the study fake activity in social networks during pre-election processes and the existing models and methods of detection of such activity. The article proposes a methodology for identifying and diagnosing induced support for a political project. The methodology includes a model of induced activity, an algorithm for segmenting the audience of a political project, and a technique for detecting and diagnosing induced support. The proposed methodology provides identification of network combatants, participants of social media special operations, influencing public opinion in the interests of a political project. The methodology can be used to raise awareness of the electorate, the public, and civil society in general about the presence of artificial activity on the page of a political project.

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Section: Related Workmentioning

confidence: 96%

Detection of Induced Activity in Social Networks: Model and Methodology

2021

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“…With the emergent role of OSNs in the 2016 U.S. presidential election, as previously mentioned, recent social bot analysis efforts have expanded their focus greatly into political OSN conversations. These works include the examination of detected bots within the 2016 U.S. presidential election [4,21,22], the UK-EU Brexit referendum [16,17], the 2018 Italian general election [46], the 2017 Catalan referendum [47] and the 2019 Spanish general election [48] within Twitter conversations. These election-focused social bot analyses relied upon an assortment of bot detection platform algorithms, but they all used a single method to classify bots.…”

Section: Introductionmentioning

confidence: 99%

“…These election-focused social bot analyses relied upon an assortment of bot detection platform algorithms, but they all used a single method to classify bots. Further, while these recent works produced promising results using a single bot detection method (e.g., Botometer in [17,21,22,48] or DeBot in [20]) and inspired the development of more robust detection algorithms, such as the vastly improved methods involving adversarial detection approaches [27,28], they ultimately do not support more robust analyses given the lack of accessibility to the underlying detection algorithms for other researchers. This study significantly expands this body of work by aggregating the classification results of three bot detection platforms (i.e., DeBot, Bot-hunter and Botometer) in an effort to provide a more holistic social bot analysis framework.…”

Section: Introductionmentioning

confidence: 99%

Insights into elections: An ensemble bot detection coverage framework applied to the 2018 U.S. midterm elections

Schuchard

Crooks

2021

PLoS ONE

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The participation of automated software agents known as social bots within online social network (OSN) engagements continues to grow at an immense pace. Choruses of concern speculate as to the impact social bots have within online communications as evidence shows that an increasing number of individuals are turning to OSNs as a primary source for information. This automated interaction proliferation within OSNs has led to the emergence of social bot detection efforts to better understand the extent and behavior of social bots. While rapidly evolving and continually improving, current social bot detection efforts are quite varied in their design and performance characteristics. Therefore, social bot research efforts that rely upon only a single bot detection source will produce very limited results. Our study expands beyond the limitation of current social bot detection research by introducing an ensemble bot detection coverage framework that harnesses the power of multiple detection sources to detect a wider variety of bots within a given OSN corpus of Twitter data. To test this framework, we focused on identifying social bot activity within OSN interactions taking place on Twitter related to the 2018 U.S. Midterm Election by using three available bot detection sources. This approach clearly showed that minimal overlap existed between the bot accounts detected within the same tweet corpus. Our findings suggest that social bot research efforts must incorporate multiple detection sources to account for the variety of social bots operating in OSNs, while incorporating improved or new detection methods to keep pace with the constant evolution of bot complexity.

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“…Some of these illicit online activities specifically have electoral contexts as their main target [3], which are ideal gaps where to maximize the influence in political behaviour and decision-making [4]. The danger this poses for democracy and freedom of thought has mobilized major companies (e.g.…”

Section: Introductionmentioning

confidence: 99%

BOTTER: A framework to analyze social bots in Twitter

Pastor-Galindo¹,

Mármol²,

Pérez³

2021

Preprint

Self Cite

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Social networks have triumphed in communicating people online, but they have also been exploited to launch influence operations for manipulating society. The deployment of software-controlled accounts (e.g., social bots) has proven to be one of the most effective enablers for that purpose, and tools for their detection have been developed and widely adopted. However, the way to analyze these accounts and measure their impact is heterogeneous in the literature, where each case study performs unique measurements. To unify these efforts, we propose a common framework to analyze the interference of social bots in Twitter. The methodology compares the nonauthentic actors with the rest of users from different perspectives, thus building objective metrics to measure their actual impact. We validate the framework by applying it to a dataset of Twitter iterations dated in the weeks preceding the 2019 Spanish general election. In this sense, we check that our framework facilitates the quantitative evaluation of unauthentic groups, particularly discovering that social bots changed the natural dynamics of the network in these days, but did not have a significant impact. We also consider this methodology as a practical tool for the qualitative interpretation of experimental results, particularly suggesting within the aforementioned electoral context that semiautomated accounts are potentially more threatening than fully automated ones.

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Spotting Political Social Bots in Twitter: A Use Case of the 2019 Spanish General Election

Cited by 41 publications

References 32 publications

Detection of Induced Activity in Social Networks: Model and Methodology

Detection of Induced Activity in Social Networks: Model and Methodology

Insights into elections: An ensemble bot detection coverage framework applied to the 2018 U.S. midterm elections

BOTTER: A framework to analyze social bots in Twitter

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