The Role of the IRA in Twitter during the 2016 US Presidential Election: Unveiling Amplification and Influence of Suspended Accounts

The impact of the social media campaign conducted by the Internet Research Agency (IRA) during the 2016 U.S. presidential election continues to be a topic of ongoing debate. While it is widely acknowledged that the objective of this campaign was to support Donald Trump, the true extent of its influence on Twitter users remains uncertain. Previous research has primarily focused on analyzing the interactions between IRA users and the broader Twitter community to assess the campaign's impact. In this study, we propose an alternative perspective that suggests the existing approach may underestimate the true extent of the IRA campaign. Our analysis uncovers the presence of a notable group of suspended Twitter users, whose size surpasses the IRA user group size by a factor of 60. These suspended users exhibit close interactions with IRA accounts, suggesting potential collaboration or coordination. Notably, our findings reveal the significant role played by these previously unnoticed accounts in amplifying the impact of the IRA campaign, surpassing even the reach of the IRA accounts themselves by a factor of 10. In contrast to previous findings, our study reveals that the combined efforts of the Internet Research Agency (IRA) and the identified group of suspended Twitter accounts had a significant influence on individuals categorized as undecided or weak supporters, probably with the intention of swaying their opinions.Comment: 13 Tables, 12 Figure

The stability of Twitter metrics: A study on unavailable Twitter mentions of scientific publications

This paper investigates the stability of Twitter counts of scientific publications over time. For this, we conducted an analysis of the availability statuses of over 2.6 million Twitter mentions received by the 1,154 most tweeted scientific publications recorded by Altmetric.com up to October 2017. Results show that of the Twitter mentions for these highly tweeted publications, about 14.3% have become unavailable by April 2019. Deletion of tweets by users is the main reason for unavailability, followed by suspension and protection of Twitter user accounts. This study proposes two measures for describing the Twitter dissemination structures of publications: Degree of Originality (i.e., the proportion of original tweets received by a paper) and Degree of Concentration (i.e., the degree to which retweets concentrate on a single original tweet). Twitter metrics of publications with relatively low Degree of Originality and relatively high Degree of Concentration are observed to be at greater risk of becoming unstable due to the potential disappearance of their Twitter mentions. In light of these results, we emphasize the importance of paying attention to the potential risk of unstable Twitter counts, and the significance of identifying the different Twitter dissemination structures when studying the Twitter metrics of scientific publications

Astroturfing as a strategy for manipulating public opinion on Twitter during the pandemic in Spain

This work aims to establish whether astroturfing was used during the Covid-19 pandemic to manipulate Spanish public opinion through Twitter. This study analyzes tweets published in Spanish and geolocated in the Philippines, and its first objective is to determine the existence of an organized network that directs its messages mainly towards Spain. To determine the non-existence of a random network, a preliminary collection of 1,496,596 tweets was carried out. After determining its 14 main clusters, 280 users with a medium-low profile of participation and micro- and nano-influencer traits were randomly selected and followed for 103 days, for a total of 309,947 tweets. Network science, text mining, sentiment and emotion, and bot probability analyses were performed using Gephi and R. Their network structure suggests an ultra-small-world phenomenon, which would determine the existence of a possible organized network that tries not to be easily identifiable. The data analyzed confirm a digital communication scenario in which astroturfing is used as a strategy aimed at manipulating public opinion through non-influencers (cybertroops). These users create and disseminate content with proximity and closeness to different groups of public opinion, mixing topics of general interest with disinformation or polarized content

Astroturfing as a strategy for manipulating public opinion on Twitter during the pandemic in Spain

This work aims to establish whether astroturfing was used during the Covid-19 pandemic to manipulate Spanish public opinion through Twitter. This study analyzes tweets published in Spanish and geolocated in the Philippines, and its first objective is to determine the existence of an organized network that directs its messages mainly towards Spain. To determine the non-existence of a random network, a preliminary collection of 1,496,596 tweets was carried out. After determining its 14 main clusters, 280 users with a medium-low profile of participation and micro- and nano-influencer traits were randomly selected and followed for 103 days, for a total of 309,947 tweets. Network science, text mining, sentiment and emotion, and bot probability analyses were performed using Gephi and R. Their network structure suggests an ultra-small-world phenomenon, which would determine the existence of a possible organized network that tries not to be easily identifiable. The data analyzed confirm a digital communication scenario in which astroturfing is used as a strategy aimed at manipulating public opinion through non-influencers (cybertroops). These users create and disseminate content with proximity and closeness to different groups of public opinion, mixing topics of general interest with disinformation or polarized content

Understanding the behaviour and influence of automated social agents

Soft-bound submitted: Fri 23 Feb 2018 Corrections submitted: Mon 30 Jul 2018 Corrections approved: Tue 7 Aug 2018 Apollo submitted: Wed 22 Aug 2018 Hard-bound submitted: Fri 24 Aug 2018Online social networks (OSNs) have seen a remarkable rise in the presence of automated social agents, or social bots. Social bots are the new computing viral, that are surreptitious and clever. What facilitates the creation of social agents is the massive human user-base and business-supportive operating model of social networks. These automated agents are injected by agencies, brands, individuals, and corporations to serve their work and purpose; utilising them for news and emergency communication, marketing, social activism, political campaigning, and even spam and spreading malicious content. Their influence was recently substantiated by coordinated social hacking and computational political propaganda. The thesis of my dissertation argues that automated agents exercise a profound impact on OSNs that transforms into an array of influence on our society and systems. However, latent or veiled, these agents can be successfully detected through measurement, feature extraction and finely tuned supervised learning models. The various types of automated agents can be further unravelled through unsupervised machine learning and natural language processing, to formally inform the populace of their existence and impact.Sep'14-Aug'17, Marie Curie ITN METRICS, Early-Stage Researcher Sep'17, UMobile, Research Associate Oct'17-Mar'18, EPSRC Global Challenges Research Fund, Research Associat

Toward automatic censorship detection in microblogs

Social media is an area where users often experience censorship through a variety of means such as the restriction of search terms or active and retroactive deletion of messages. In this paper we examine the feasibility of automatically detecting censorship of microblogs. We use a network growing model to simulate discussion over a microblog follow network and compare two censorship strategies to simulate varying levels of message deletion. Using topological features extracted from the resulting graphs, a classifier is trained to detect whether or not a given communication graph has been censored. The results show that censorship detection is feasible under empirically measured levels of message deletion. The proposed framework can enable automated censorship measurement and tracking, which, when combined with aggregated citizen reports of censorship, can allow users to make informed decisions about online communication habits.Comment: 13 pages. Updated with example cascades figure and typo fixes. To appear at the International Workshop on Data Mining in Social Networks (PAKDD-SocNet) 201

Discovering and Mitigating Social Data Bias

abstract: Exabytes of data are created online every day. This deluge of data is no more apparent than it is on social media. Naturally, finding ways to leverage this unprecedented source of human information is an active area of research. Social media platforms have become laboratories for conducting experiments about people at scales thought unimaginable only a few years ago. Researchers and practitioners use social media to extract actionable patterns such as where aid should be distributed in a crisis. However, the validity of these patterns relies on having a representative dataset. As this dissertation shows, the data collected from social media is seldom representative of the activity of the site itself, and less so of human activity. This means that the results of many studies are limited by the quality of data they collect. The finding that social media data is biased inspires the main challenge addressed by this thesis. I introduce three sets of methodologies to correct for bias. First, I design methods to deal with data collection bias. I offer a methodology which can find bias within a social media dataset. This methodology works by comparing the collected data with other sources to find bias in a stream. The dissertation also outlines a data collection strategy which minimizes the amount of bias that will appear in a given dataset. It introduces a crawling strategy which mitigates the amount of bias in the resulting dataset. Second, I introduce a methodology to identify bots and shills within a social media dataset. This directly addresses the concern that the users of a social media site are not representative. Applying these methodologies allows the population under study on a social media site to better match that of the real world. Finally, the dissertation discusses perceptual biases, explains how they affect analysis, and introduces computational approaches to mitigate them. The results of the dissertation allow for the discovery and removal of different levels of bias within a social media dataset. This has important implications for social media mining, namely that the behavioral patterns and insights extracted from social media will be more representative of the populations under study.Dissertation/ThesisDoctoral Dissertation Computer Science 201

A Study of Norm Formation Dynamics in Online Crowds

In extreme events such as the Egyptian 2011 uprising, online social media technology enables many people from heterogeneous backgrounds to interact in response to the crisis. This form of collectivity (an online crowd) is usually formed spontaneously with minimum constraints concerning the relationships among the members. Theories of collective behavior suggest that the patterns of behavior in a crowd are not just a set of random acts. Instead they evolve toward a normative stage. Because of the uncertainty of the situations people are more likely to search for norms. Understanding the process of norm formation in online social media is beneficial for any organization that seeks to establish a norm or understand how existing norms emerged. In this study, I propose a longitudinal data-driven approach to investigate the dynamics of norm formation in online crowds. In the research model, the formation of recurrent behaviors (behavior regularities) is recognized as the first step toward norm formation; and the focus of this study is on the first step. The dataset is the tweets posted during the Egyptian 2011 movement. The results show that the social structure has impact on the formation of behavioral regularities, which is the first step of norm formation. Also, the results suggest that accounting for different roles in the crowd will uncover a more detailed view of norm and help to define emergent norm from a new perspective. The outcome indicates that there are significant differences in behavioral regularities between different roles formed over time. For instance, the users of the same role tend to practice more reciprocity inside their role group rather than outside of their role. I contribute to theory first by extending the implications of current relevant theories to the context of online social media, and second by investigating theoretical implications through an analysis of empirical real-life data. In this dissertation, I review prior studies and provide the theoretical foundation for my research. Then I discuss the research method and the preliminary results from the pilot studies. I present the results from the analysis and provide a discussion and conclusion

Unmasking the Web of Deceit: Uncovering Coordinated Activity to Expose Information Operations on Twitter

Social media platforms, particularly Twitter, have become pivotal arenas for influence campaigns, often orchestrated by state-sponsored information operations (IOs). This paper delves into the detection of key players driving IOs by employing similarity graphs constructed from behavioral pattern data. We unveil that well-known, yet underutilized network properties can help accurately identify coordinated IO drivers. Drawing from a comprehensive dataset of 49 million tweets from six countries, which includes multiple verified IOs, our study reveals that traditional network filtering techniques do not consistently pinpoint IO drivers across campaigns. We first propose a framework based on node pruning that emerges superior, particularly when combining multiple behavioral indicators across different networks. Then, we introduce a supervised machine learning model that harnesses a vector representation of the fused similarity network. This model, which boasts a precision exceeding 0.95, adeptly classifies IO drivers on a global scale and reliably forecasts their temporal engagements. Our findings are crucial in the fight against deceptive influence campaigns on social media, helping us better understand and detect them.Comment: Accepted at the 2024 ACM Web Conferenc