TrollHunter [Evader]: Automated Detection [Evasion] of Twitter Trolls During the COVID-19 Pandemic

Jachim, Peter; Sharevski, Filipo; Treebridge, Paige

doi:10.1145/3442167.3442169

Cited by 18 publications

(27 citation statements)

References 53 publications

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“…Our results suggest that people are overly sensitive to pessimistic rumours about the COVID-19 vaccine, as well as alternative hashtags [7]. Our results showed that it was sufficient for a Tweet to cause a misperception of otherwise valid content was not very accurate through the inclusion of popular alternative hashtags #COVIDIOT and #covidhoax.…”

Section: Introductionmentioning

confidence: 72%

“…This is a promising result suggesting that Twitter users in our liberalleaning sample can accurately assess an attempt for spreading rumours about this vital operation for mass immunization. Perhaps this is an expected result given that liberal-leaning, and possibly moderate users, are sensitive to any attempt to tarnish the actions of Donald Trump, who is widely accepted as the top misinformation machine over the last four years [7]. Or these participants closely monitor mainstream media compared to their conservative counterparts [23].…”

Section: B Covid-19 Vaccine Perceptions On Mass Immunizationmentioning

confidence: 93%

“…The software also negated the word "could" to "couldn't" and inserted the word "lasting" before the word "immunity" to emphasize the lack of evidence about the length of the immunity provided by this particular COVID-19 vaccine at the time of the study [14]. The software also inserted two trending hashtags among top alternative COVID-19 Twitter users, #COVIDIOT and #covidhoax [7], [15]. The misinformation labels on Twitter gained widespread attention with the soft (and later hard) moderation of political content [1].…”

Section: A Covid-19 Vaccine Misperceptionsmentioning

confidence: 99%

“…For the second test of misperception of COVID-19 vaccines we opted to test a more politicized Tweet. COVID-19 was one of the main focal points of the political battle during and after the U.S. elections in 2020 that naturally flooded over to Twitter [7]. Therefore, we tested a Tweet regarding the new administration's intentions for renaming "Operation Warp Speed," the Department of Defense's effort for rapid U.S. mass immunization [21].…”

Section: B Covid-19 Vaccine Perceptions On Mass Immunizationmentioning

confidence: 99%

See 3 more Smart Citations

(Mis)perceptions and Engagement on Twitter: COVID-19 Vaccine Rumors on Efficacy and Mass Immunization Effort

Sharevski¹,

Allice²,

Jachim³

et al. 2021

Preprint

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This paper reports the findings of a 606-participant study where we analyzed the perception and engagement effects of COVID-19 vaccine rumours on Twitter pertaining to (a) vaccine efficacy; and (b) mass immunization efforts in the United States. Misperceptions regarding vaccine efficacy were successfully induced through simple content alterations and the addition of popular anti COVID-19 hashtags to otherwise valid Twitter content. Twitter's misinformation contextual tags caused a "backfire effect" for the skeptic, vaccine-hesitant participants reinforcing their opposition stance. While the majority of the participants staunchly refrained from engaging with the COVID-19 rumours, the vaccine-hesitant ones were open to comment, re-tweet, like and share the vaccine efficacy rumors. We discuss the implications of our results in the context of broadening the effort for dispelling rumors about COVID-19 on social media.

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Section: Introductionmentioning

confidence: 72%

Section: B Covid-19 Vaccine Perceptions On Mass Immunizationmentioning

confidence: 93%

Section: A Covid-19 Vaccine Misperceptionsmentioning

confidence: 99%

Section: B Covid-19 Vaccine Perceptions On Mass Immunizationmentioning

confidence: 99%

See 2 more Smart Citations

(Mis)perceptions and Engagement on Twitter: COVID-19 Vaccine Rumors on Efficacy and Mass Immunization Effort

Sharevski¹,

Allice²,

Jachim³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…In doing so, they have also created opportunities for cyber-attackers and cyber-trolls to disseminate misinformation, offend and cyber-bully the users, and cause disputes through social media and public forums. [120] describe online trolls as malicious users pretending to be sincere members of a group discussion but subtly attempting to disrupt the discussion and cause conflict. [121] claim that the lack of attention to social science by the designers and developers of social media is responsible for spreading misinformation.…”

Section: Clickbaits and Spamsmentioning

confidence: 99%

Adversarial Attacks and Defenses for Social Network Text Processing Applications: Techniques, Challenges and Future Research Directions

Alsmadi¹,

Ahmad²,

Nazzal³

et al. 2021

Preprint

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The growing use of social media has led to the development of several Machine Learning (ML) and Natural Language Processing (NLP) tools to process the unprecedented amount of social media content to make actionable decisions. However, these ML and NLP algorithms have been widely shown to be vulnerable to adversarial attacks. These vulnerabilities allow adversaries to launch a diversified set of adversarial attacks on these algorithms in different applications of social media text processing. In this paper, we provide a comprehensive review of the main approaches for adversarial attacks and defenses in the context of social media applications with a particular focus on key challenges and future research directions. In detail, we cover literature on six key applications, namely (i) rumors detection, (ii) satires detection, (iii) clickbaits & spams identification, (iv) hate speech detection, (v) misinformation detection, and (vi) sentiment analysis. We then highlight the concurrent and anticipated future research questions and provide recommendations and directions for future work.

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Meta-modelling for Ecosystems Security

Caulfield

Ilau

Pym

2022

Simulation Tools and Techniques

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As the world has evolved to become ever more dependent on complex ecosystems of large, interacting systems, it has become ever more important to be able to reason rigorously about the design, construction, and behaviour not only of individual systems -which may include aspects related to all of people, process, and technology -but also of their assembly into ecosystems. In such situations, it is inevitable that no one type of model -such as mathematical models of dynamical systems, logical models of languages, or discrete event simulation models -will be sufficient to describe all of the aspects of ecosystems about which rigorous reasoning is required. We propose here a metatheoretical framework, the 'triangle framework', within which different types of models may be categorized and their interactions, especially during the construction of models, can be understood. Its explicit goals are to facilitate a better understanding of the nature of models and to provide a more inclusive language for the description of heterogeneous models. Specifically, we identify three qualities of models, each derived from modelling goals -conceptuality, mathematicality, and executability -and explain how models will, typically, have all of these qualities to varying extents. We also show how the framework supports an analysis of how models can be co-designed by their various stakeholders within an identified translation zone within the process of model construction. We explore our ideas in the concrete setting of models encountered in a range of surveyed security papers, drawn from a diverse collection of security conferences. Although descriptive in nature, we envision this framework as a necessary first step in the development of a methodology for heterogeneous model design and construction, diverse enough to characterize the myriad of model types used in the field of information security while at the same time addressing validation concerns that can reduce their usability in the area of security decision-making.

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TrollHunter [Evader]: Automated Detection [Evasion] of Twitter Trolls During the COVID-19 Pandemic

Cited by 18 publications

References 53 publications

(Mis)perceptions and Engagement on Twitter: COVID-19 Vaccine Rumors on Efficacy and Mass Immunization Effort

(Mis)perceptions and Engagement on Twitter: COVID-19 Vaccine Rumors on Efficacy and Mass Immunization Effort

Adversarial Attacks and Defenses for Social Network Text Processing Applications: Techniques, Challenges and Future Research Directions

Meta-modelling for Ecosystems Security

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