The capability to reach a wider audience and the possibility to disseminate news faster are the main reasons for the growing importance of Online Social Media (OSM) whose usage has undoubtedly reshaped the way news are written, published and disseminated. However, due to the technical limits of online fact-checkers and to an uncontrolled content publishing, there is a high risk of being misinformed through fake news. Although automated accounts known as bots are considered the main promoters of mis-/dis- information diffusion, those who, with their actions, change the current events (e.g., welfare, economy, politics, etc.) are human users. Some categories of humans are more vulnerable to fake news than others, and performing mis-/dis- information activities targeting such categories would increase the efficacy of such activities. Furthermore, recent studies have evidenced users' attitude not to fact-check the news they diffuse on OSM and thus the risk that they became themselves vectors of mis-/dis- information. In this document, using Twitter as a benchmark, we devote our attention to those human-operated accounts, named ``credulous'' users, which show a relatively high number of bots as followees (called bot-followees). We believe that these users are more vulnerable to manipulation (w.r.t. other human-operated accounts) and, although unknowingly, they can be involved in malicious activities such as diffusion of fake content. Specifically, we first design some heuristics by focusing on the aspects that best characterise the credulous users w.r.t. not credulous ones. Then, by applying Machine Learning (ML) techniques, we develop an approach based on binary classifiers able to automatically identify this kind of users and then use regression models to predict the percentage of humans' bot-followees (over their respective followees). Subsequently, we describe investigations conducted to ascertain the actual contribution of credulous users in the dissemination of potentially malicious content and then, their involvements in fake news diffusion by analysing and comparing the fake news spread by credulous users w.r.t. not credulous one. Our investigations and experiments provide evidence of credulous users' involvement in spreading fake news thus supporting bots' actions on OSM.

Potential target audience of misinformation on Social Media: Credulous Users / Balestrucci, Alessandro. - (2020 Dec 18).

Potential target audience of misinformation on Social Media: Credulous Users

BALESTRUCCI, ALESSANDRO
2020-12-18

Abstract

The capability to reach a wider audience and the possibility to disseminate news faster are the main reasons for the growing importance of Online Social Media (OSM) whose usage has undoubtedly reshaped the way news are written, published and disseminated. However, due to the technical limits of online fact-checkers and to an uncontrolled content publishing, there is a high risk of being misinformed through fake news. Although automated accounts known as bots are considered the main promoters of mis-/dis- information diffusion, those who, with their actions, change the current events (e.g., welfare, economy, politics, etc.) are human users. Some categories of humans are more vulnerable to fake news than others, and performing mis-/dis- information activities targeting such categories would increase the efficacy of such activities. Furthermore, recent studies have evidenced users' attitude not to fact-check the news they diffuse on OSM and thus the risk that they became themselves vectors of mis-/dis- information. In this document, using Twitter as a benchmark, we devote our attention to those human-operated accounts, named ``credulous'' users, which show a relatively high number of bots as followees (called bot-followees). We believe that these users are more vulnerable to manipulation (w.r.t. other human-operated accounts) and, although unknowingly, they can be involved in malicious activities such as diffusion of fake content. Specifically, we first design some heuristics by focusing on the aspects that best characterise the credulous users w.r.t. not credulous ones. Then, by applying Machine Learning (ML) techniques, we develop an approach based on binary classifiers able to automatically identify this kind of users and then use regression models to predict the percentage of humans' bot-followees (over their respective followees). Subsequently, we describe investigations conducted to ascertain the actual contribution of credulous users in the dissemination of potentially malicious content and then, their involvements in fake news diffusion by analysing and comparing the fake news spread by credulous users w.r.t. not credulous one. Our investigations and experiments provide evidence of credulous users' involvement in spreading fake news thus supporting bots' actions on OSM.
Disinformation, Credulous users, Twitter, Machine Learning, Behavioural Analysis, Fake News
Potential target audience of misinformation on Social Media: Credulous Users / Balestrucci, Alessandro. - (2020 Dec 18).
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.12571/14754
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