Characterizing Social Bots Spreading Financial Disinformation

Tardelli, Serena; Avvenuti, Marco; Tesconi, Maurizio; Cresci, Stefano

doi:10.1007/978-3-030-49570-1_26

Cited by 17 publications

(16 citation statements)

References 27 publications

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“… Feature Description Whether the Feature can be computed from Twitter API Recent Studies 1. Total hashtag Count of hashtags in a tweet Yes [ 29 , 35 , 36 , 41 , 43 , 46 ] 2. Total link Count of link in a tweet Yes [ 29 , 33 , 35 , 43 , 44 ] 3.…”

Section: Related Workmentioning

confidence: 99%

“… Total link Count of link in a tweet Yes [ 29 , 33 , 35 , 43 , 44 ] 3. Total mention Count of users mentions in a tweet Yes [ 35 , 36 , 41 ] 4. Total retweet Count of retweets received by a tweet Yes [ 29 , 33 , 36 , 43 ] 5.…”

Section: Related Workmentioning

confidence: 99%

“… Unique hashtag Count of unique hashtags used in all tweets. No [ 41 ] 25. Unique mention Count of unique mentions in all tweets.…”

Section: Related Workmentioning

confidence: 99%

“… Unique mention Count of unique mentions in all tweets. No [ 41 ] 26. Maximum frequency of hashtag Hashtag with maximum frequency in all tweets.…”

Section: Related Workmentioning

confidence: 99%

See 3 more Smart Citations

Role of twitter user profile features in retweet prediction for big data streams

Sharma

Gupta

2022

Multimed Tools Appl

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To study the various factors influencing the process of information sharing on Twitter is a very active research area. This paper aims to explore the impact of numerical features extracted from user profiles in retweet prediction from the real-time raw feed of tweets. The originality of this work comes from the fact that the proposed model is based on simple numerical features with the least computational complexity, which is a scalable solution for big data analysis. This research work proposes three new features from the tweet author profile to capture the unique behavioral pattern of the user, namely “Author total activity”, “Author total activity per year”, and “Author tweets per year”. The features set is tested on a dataset of 100 million random tweets collected through Twitter API. The binary labels regression gave an accuracy of 0.98 for user-profile features and gave an accuracy of 0.99 when combined with tweet content features. The regression analysis to predict the retweet count gave an R-squared value of 0.98 with combined features. The multi-label classification gave an accuracy of 0.9 for combined features and 0.89 for user-profile features. The user profile features performed better than tweet content features and performed even better when combined. This model is suitable for near real-time analysis of live streaming data coming through Twitter API and provides a baseline pattern of user behavior based on numerical features available from user profiles only.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“… Unique hashtag Count of unique hashtags used in all tweets. No [ 41 ] 25. Unique mention Count of unique mentions in all tweets.…”

Section: Related Workmentioning

confidence: 99%

“… Unique mention Count of unique mentions in all tweets. No [ 41 ] 26. Maximum frequency of hashtag Hashtag with maximum frequency in all tweets.…”

Section: Related Workmentioning

confidence: 99%

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Role of twitter user profile features in retweet prediction for big data streams

Sharma

Gupta

2022

Multimed Tools Appl

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“…False narratives, fake accounts, low-credibility news sources, state-sponsored operators, and so on and so forth: The online ecosystem landscape appears loaded with threats and malicious entities disposed to undermine the integrity of social media discussions. Among those, bots (i.e., automated and softwarecontrolled accounts) [23,12] and trolls (i.e., human operators often statesponsored) [61,35,3] have been recognized as the main responsible actors of manipulation and misinformation operations in diverse contexts [18], ranging from finance [38,14,51] to public health [21,58], in which the rise of infodemics (i.e., the widespread diffusion of unverified information and conspiracy theories) during the Covid-19 outbreak represents the latest milestone of the misinformation age [63]. Moreover, the abusive behavior of these malicious actors received enormous resonance in the political domain [33,5,37,29,2,4,25,62,34], where the abuse of social platforms has put under threat the effective fulfillment of the democratic process, other than creating worldwide concerns for the integrity of voting events [47,7,49,3,40,19,28,46,54,48,6,27,43,41].…”

Section: Introductionmentioning

confidence: 99%

Down the bot hole: actionable insights from a 1-year analysis of bots activity on Twitter

Luceri¹,

Cardoso²,

Giordano³

2020

Preprint

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Nowadays, social media represent persuasive tools that have been progressively weaponized to affect people's beliefs, spread manipulative narratives, and sow conflicts along divergent factions. Softwarecontrolled accounts (i.e., bots) are one of the main actors associated with manipulation campaigns, especially in the political context. Uncovering the strategies behind bots' activities is of paramount importance to detect and curb such campaigns. In this paper, we present a long term (one year) analysis of bots activity on Twitter in the run-up to the 2018 U.S. Midterm Elections. We identify different classes of accounts based on their nature (bot vs. human) and engagement within the online discussion and we observe that hyperactive bots played a pivotal role in the dissemination of conspiratorial narratives, while dominating the political debate since the year before the election. Our analysis, on the horizon of the upcoming U.S. 2020 Presidential Election, reveals both alarming findings of humans' susceptibility to bots and actionable insights that can contribute to curbing coordinated campaigns.

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