Article

Disinformation and Social Bot Operations in the Run Up to the 2017 French Presidential Election

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Abstract

Recent accounts from researchers, journalists, as well as federal investigators, reached a unanimous conclusion: social media are systematically exploited to manipulate and alter public opinion. Some disinformation campaigns have been coordinated by means of bots, social media accounts controlled by computer scripts that try to disguise themselves as legitimate human users. In this study, we describe one such operation occurred in the run up to the 2017 French presidential election. We collected a massive Twitter dataset of nearly 17 million posts occurred between April 27 and May 7, 2017 (Election Day). We then set to study the MacronLeaks disinformation campaign: By leveraging a mix of machine learning and cognitive behavioral modeling techniques, we separated humans from bots, and then studied the activities of the two groups taken independently, as well as their interplay. We provide a characterization of both the bots and the users who engaged with them and oppose it to those users who didn't. Prior interests of disinformation adopters pinpoint to the reasons of the scarce success of this campaign: the users who engaged with MacronLeaks are mostly foreigners with a preexisting interest in alt-right topics and alternative news media, rather than French users with diverse political views. Concluding, anomalous account usage patterns suggest the possible existence of a black-market for reusable political disinformation bots.

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... These platforms provide fertile ground for disinformation campaigns, where misleading or fabricated content can be seamlessly woven into immersive experiences, making it harder for users to differentiate between fact and fiction [134,140]. The convergence of AI, virtual worlds, and real-time user interaction heightens the risk of psychological and social manipulation, potentially transforming XR spaces into tools for influencing public opinion on a large scale [141,142]. ...
... Virtual experiences are highly immersive, and the emotional and cognitive impact they can have on users often exceeds that of traditional media. In such contexts, individuals may be exposed to "echo chambers", where their beliefs and opinions are reinforced by interacting with only those ideas that align with their pre-existing views, exacerbating political polarization [128,142]. Misinformation in XR environments is more than just textual or visual; it is experiential, making it harder for users to critically evaluate the information presented to them [143]. ...
... This makes XR an ideal platform for orchestrating campaigns designed to sway public opinion or influence individual behaviors [125]. Virtual Reality simulations that portray emotionally charged or controversial content could manipulate users' emotions to create fear, loyalty, or anger, effectively shaping how they view real-world events or political issues [142]. The use of cognitive biases, such as emotional appeal and repetition, in immersive environments makes it easier for disinformation campaigns to alter perceptions and beliefs subtly. ...
Preprint
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... The last few years have seen a deluge of increasingly more sophisticated automated online agents, called also "bots", populating techno-social systems cleverly disguised as human users [5][6][7][8][9]. Nowadays, bots can produce credible content with human-like temporal patterns [10][11][12]. By promoting online activity, bots can interact with humans and influence their standing against specific topics such as political issues [7,10,12,13]. ...
... To identify automated agents in the data set, we developed a deep neural network model (see Methods and SI), which classified 13.4% of users as bots, a value compatible to estimations during other voting events [10][11][12]. We built the network of interactions between human users and bots, including different types of social actions such as Retweets (i.e. a user sharing another user's message), Mentions (i.e. a user mentioning another use in a message) and Replies (i.e. a user starting a discussion with another user). ...
... Classification task. In this work the classification of users in our data set as "humans" or "bots" is based on features providing the best classification accuracy according to recent studies [11]: 1) Statuses count; 2) Followers count; 3) Friends count; 4) Favourites count; 5) Listed count; 6) Default profile; 7) Geo enabled ; 8) Profile use background image; 9) Protected ; 10) Verified for a total of ten features (N f eats = 10). ...
Preprint
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... These platforms provide fertile ground for disinformation campaigns, where misleading or fabricated content can be seamlessly woven into immersive experiences, making it harder for users to differentiate between fact and fiction [134], [140]. The convergence of AI, virtual worlds, and real-time user interaction heightens the risk of psychological and social manipulation, potentially transforming XR spaces into tools for influencing public opinion on a large scale [141], [142]. ...
... Virtual experiences are highly immersive, and the emotional and cognitive impact they can have on users often exceeds that of traditional media. In such contexts, individuals may be exposed to "echo chambers," where their beliefs and opinions are reinforced by interacting with only those ideas that align with their pre-existing views, exacerbating political polarization [128], [142]. ...
... This makes XR an ideal platform for orchestrating campaigns designed to sway public opinion or influence individual behaviors [125]. Virtual reality simulations that portray emotionally charged or controversial content could manipulate users' emotions to create fear, loyalty, or anger, effectively shaping how they view real-world events or political issues [142]. The use of cognitive biases, such as emotional appeal and repetition, in immersive environments makes it easier for disinformation campaigns to alter perceptions and beliefs subtly. ...
Preprint
Full-text available
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... These platforms provide fertile ground for disinformation campaigns, where misleading or fabricated content can be seamlessly woven into immersive experiences, making it harder for users to differentiate between fact and fiction [157,161]. The convergence of AI, virtual worlds, and realtime user interaction heightens the risk of psychological and social manipulation, potentially transforming XR spaces into tools for influencing public opinion on a large scale [162,163]. ...
... Virtual experiences are highly immersive, and the emotional and cognitive impact they can have on users often exceeds that of traditional media. In such contexts, individuals may be exposed to "echo chambers," where their beliefs and opinions are reinforced by interacting with only those ideas that align with their pre-existing views, exacerbating political polarization [152,163]. Misinformation in XR environments is more than just textual or visual; it is experiential, making it harder for users to critically evaluate the information presented to them [164]. ...
... This makes XR an ideal platform for orchestrating campaigns designed to sway public opinion or influence individual behaviors [149]. Virtual reality simulations that portray emotionally charged or controversial content could manipulate users' emotions to create fear, loyalty, or anger, effectively shaping how they view real-world events or political issues [163]. The use of cognitive biases, such as emotional appeal and repetition, in immersive environments makes it easier for disinformation campaigns to alter perceptions and beliefs subtly. ...
Preprint
Full-text available
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... These platforms provide fertile ground for disinformation campaigns, where misleading or fabricated content can be seamlessly woven into immersive experiences, making it harder for users to differentiate between fact and fiction [157,161]. The convergence of AI, virtual worlds, and realtime user interaction heightens the risk of psychological and social manipulation, potentially transforming XR spaces into tools for influencing public opinion on a large scale [162,163]. ...
... Virtual experiences are highly immersive, and the emotional and cognitive impact they can have on users often exceeds that of traditional media. In such contexts, individuals may be exposed to "echo chambers," where their beliefs and opinions are reinforced by interacting with only those ideas that align with their pre-existing views, exacerbating political polarization [152,163]. Misinformation in XR environments is more than just textual or visual; it is experiential, making it harder for users to critically evaluate the information presented to them [164]. ...
... This makes XR an ideal platform for orchestrating campaigns designed to sway public opinion or influence individual behaviors [149]. Virtual reality simulations that portray emotionally charged or controversial content could manipulate users' emotions to create fear, loyalty, or anger, effectively shaping how they view real-world events or political issues [163]. The use of cognitive biases, such as emotional appeal and repetition, in immersive environments makes it easier for disinformation campaigns to alter perceptions and beliefs subtly. ...
Preprint
Full-text available
This scoping review explores the intersection of Extended Mind Theory and Extended Reality (XR) technologies, focusing on how Virtual Reality, Augmented Reality, and Mixed Reality reshape human cognition and interaction. XR enables users to offload cognitive tasks and engage in embodied experiences, extending cognition beyond the brain into digital environments. The review highlights a wide range of XR applications, from immersive learning in STEM education and medical training, neuropsychological assessment to therapeutic interventions, arts and entertainment, professional skills development, retail and e-commerce, remote work, sports training, architecture and urban planning, and cultural heritage preservation. XR's integration with modalities like haptics, eye-tracking, face- and body-tracking, and brain-computer interfaces further enhances cognitive extension and user engagement. However, alongside these advancements come significant ethical, psychological, and societal challenges, such as data privacy concerns, the psychological effects of prolonged immersion, and social inequality arising from disparate access to XR technologies. This review emphasizes the need for robust ethical frameworks that address these challenges, ensuring that XR technologies enhance human development while maintaining autonomy, privacy, and mental well-being. As XR continues to evolve and integrate with artificial intelligence and other emerging technologies, its role in expanding human cognition will depend on responsible implementation and governance.
... Although these platforms have invested heavily to remove harmful accounts, malicious actors have adapted their strategies to evade detection and develop increasingly sophisticated influence campaigns (Sayyadiharikandeh et al. 2020). Technologies have been developed to detect, characterize, and track inauthentic account activity at scale (Ferrara 2017;Sayyadiharikandeh et al. 2020;Paper 2022), but there is a pressing need to better understand the tactics and strategies of influence campaigns that utilize inauthentic accounts through analysis of the content they promote. ...
... In this paper, we analyze a large corpus of over 5M tweets related to the 2017 French presidential election to identify influence campaigns intended to affect the outcome of the election (Ferrara 2017). We use well-understood heuristics to identify coordinated inauthentic accounts (Pacheco et al. 2021) (we call these "coordinated accounts" for brevity) that may be attempting to influence election outcomes. ...
... Our motivation to analyze the 2017 election in particular is because there was a leak of French presidential candidate Emmanuel Macron's campaign emails (#MacronLeaks) on 5 May, just before the second round of voting on 7 May. #MacronLeaks leveraged a large cache of hacked documents and emails shared on WikiLeaks to discredit Macron and his party, En Marche (Ferrara 2017;Vilmer 2021), likely orchestrated by Russia (Gray 2017). It was exposed on the imageboard 4Chan and tweeted on 5 May by American alt-right activist Jack Posobiec (Gray 2017). ...
Article
Online manipulation is a pressing concern for democracies, but the actions and strategies of coordinated inauthentic accounts, which have been used to interfere in elections, are not well understood. We analyze a five million-tweet multilingual dataset related to the 2017 French presidential election, when a major information campaign led by Russia called "#MacronLeaks" took place. We utilize heuristics to identify coordinated inauthentic accounts and detect attitudes, concerns and emotions within their tweets, collectively known as socio-linguistic characteristics. We find that coordinated accounts retweet other coordinated accounts far more than expected by chance, while being exceptionally active just before the second round of voting. Concurrently, socio-linguistic characteristics reveal that coordinated accounts share tweets promoting a candidate at three times the rate of non-coordinated accounts. Coordinated account tactics also varied in time to reflect news events and rounds of voting. Our analysis highlights the utility of socio-linguistic characteristics to inform researchers about tactics of coordinated accounts and how these may feed into online social manipulation.
... These frequently serve as weapons to mislead audiences and further particular objectives. All of these traits have contributed to the growth of computational propaganda, in which automated algorithms sway public opinion, undermine institutional credibility, and affect political results (Ferrara, 2020). ...
... The difficulty of recognizing and thwarting AI-generated content, which is sometimes indistinguishable from real information, adds to the difficulties, according to Vosoughi, Roy, and Aral (2018). Ferrara (2020) goes into detail into the moral conundrums raised by the creation and spread of false information using AI. There are concerns over responsibility and the procedures for regulating such technologies given how easily generative AI tools may create hyper-realistic content. ...
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The purpose of this study was to investigate how artificial intelligence (AI) influences and improves computational propaganda and misinformation efforts. The growing complexity of AI-driven technologies, like deepfakes, bots, and algorithmic manipulation, which have turned conventional propaganda strategies into more widespread and damaging media manipulation techniques, served as the researcher's inspiration. The study used a mixed-methods approach, combining quantitative data analysis from academic studies and digital forensic investigations with qualitative case studies of misinformation efforts. The results brought to light important tactics including the platform-specific use of X (formerly Twitter) to propagate false information, emotional exploitation through fear-based messaging, and purposeful amplification through bot networks. According to this research, AI technologies enhanced controversial content by taking use of algorithmic biases, so generating echo chambers and eroding confidence in democratic processes. The study also emphasized how deepfake technologies and their ability to manipulate susceptible populations' emotions present ethical and sociopolitical issues. In order to counteract AI-generated misinformation, the study suggested promoting digital literacy and creating more potent detection methods, such digital watermarking. Future studies should concentrate on the long-term psychological effects of AI-driven misinformation on democratic participation, public trust, and regulatory reactions in various countries. Furthermore, investigating how new AI technologies are influencing other media, like video games and virtual reality, may help humans better comprehend how they affect society as a whole.
... Misinformation is an instance of the broader issue of abuse of social media platforms, which has received a lot of attention in the recent literature [4][5][6][7][8][9][10][11][12][13][14][15]. The traditional method to cope with misinformation is to fact-check claims. ...
... Different sources will be of course active in different countries. Worrisome amounts of misinformation, for example, have been observed in the run-up to the general elections in France [14]. To foster the study of misinformation in non-US contexts, we have released the code of Hoaxy under an open-source license, so that other groups can build upon our work [59,60]. ...
Preprint
Massive amounts of fake news and conspiratorial content have spread over social media before and after the 2016 US Presidential Elections despite intense fact-checking efforts. How do the spread of misinformation and fact-checking compete? What are the structural and dynamic characteristics of the core of the misinformation diffusion network, and who are its main purveyors? How to reduce the overall amount of misinformation? To explore these questions we built Hoaxy, an open platform that enables large-scale, systematic studies of how misinformation and fact-checking spread and compete on Twitter. Hoaxy filters public tweets that include links to unverified claims or fact-checking articles. We perform k-core decomposition on a diffusion network obtained from two million retweets produced by several hundred thousand accounts over the six months before the election. As we move from the periphery to the core of the network, fact-checking nearly disappears, while social bots proliferate. The number of users in the main core reaches equilibrium around the time of the election, with limited churn and increasingly dense connections. We conclude by quantifying how effectively the network can be disrupted by penalizing the most central nodes. These findings provide a first look at the anatomy of a massive online misinformation diffusion network.
... This amplification effect is particularly problematic during crises when accurate information is crucial for public safety and effective crisis management (Cinelli et al., 2020). Furthermore, the anonymity and low barriers to entry on social media platforms facilitate the creation and spread of fake news by malicious actors, including trolls, bots, and foreign agents seeking to manipulate public opinion (Ferrara, 2017). These actors exploit the virality potential of social media to disseminate misinformation rapidly and widely, often using coordinated campaigns to target specific groups or issues. ...
... The anonymity and low entry barriers provided by social media also facilitate the creation and dissemination of fake news. Malicious actors, including trolls, bots, and coordinated disinformation campaigns, exploit these features to spread false information rapidly and widely, often targeting vulnerable groups or exploiting existing societal divisions (Ferrara, 2017). In crisis situations, these actors can exacerbate fear, panic, and confusion by deliberately spreading misinformation that undermines trust in official sources and disrupts effective crisis management (Starbird, 2017). ...
Article
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The proliferation of fake news has become a significant concern, particularly during crisis situations when accurate information is crucial for public safety and decision-making. This article explores the impact of fake news on public opinion during crises, analyzing how misinformation spreads and influences perceptions and behaviors. Fake news often leverages the heightened emotions and uncertainties of crises, such as natural disasters, pandemics, or political turmoil, to manipulate public opinion, exacerbate panic, and erode trust in legitimate sources. The paper examines the psychological mechanisms that make individuals more susceptible to fake news, including cognitive biases and the tendency to seek information that aligns with pre-existing beliefs. It also investigates the role of social media platforms in amplifying misinformation, where algorithms prioritize sensational content that often includes false or misleading information. Through case studies and empirical research, this article highlights the consequences of fake news on public opinion, such as the spread of fear, the polarization of communities, and the challenge of implementing effective crisis management strategies. To counteract these effects, the article suggests a multi-faceted approach, including media literacy education, enhanced fact-checking practices, and robust policies to regulate misinformation online. This research aims to contribute to a deeper understanding of the relationship between fake news and public opinion during crises, advocating for more resilient communication strategies to maintain public trust and ensure informed decision-making.
... The increasing prevalence of information operations online has led to a large body of scholarship on their detection. This detection work has predominantly focused on the social media platform X (formerly known as Twitter) and has explored various approaches to identify coordinated behavior online -an indicator of a information campaign [18,20,27,45,58]. Detection work related to Wikipedia is often focused on sockpuppet accounts -where an editor user misuses multiple accounts, often to deceive other editors or evade bans [41,55]. ...
... Several participants noted that EC page protections evolved from, in part, dealing with the contentious topic of Israel-Palestine. 27 For the Russo-Ukrainian War articles, the EC protections were widely described as successful by participants. Participant 09 said: ...
Preprint
Full-text available
How do Wikipedians maintain an accurate encyclopedia during an ongoing geopolitical conflict where state actors might seek to spread disinformation or conduct an information operation? In the context of the Russia-Ukraine War, this question becomes more pressing, given the Russian government's extensive history of orchestrating information campaigns. We conducted an interview study with 13 expert Wikipedians involved in the Russo-Ukrainian War topic area on the English-language edition of Wikipedia. While our participants did not perceive there to be clear evidence of a state-backed information operation, they agreed that war-related articles experienced high levels of disruptive editing from both Russia-aligned and Ukraine-aligned accounts. The English-language edition of Wikipedia had existing policies and processes at its disposal to counter such disruption. State-backed or not, the disruptive activity created time-intensive maintenance work for our participants. Finally, participants considered English-language Wikipedia to be more resilient than social media in preventing the spread of false information online. We conclude by discussing sociotechnical implications for Wikipedia and social platforms.
... Disinformation Campaigns. Numerous studies have examined the role of social bots in the spread of political disinformation [7], [19], [20], [57]. This body of work demonstrates that bots are capable of large-scale public opinion manipulation, which may have an impact on important political events, such as election results. ...
... I.e., it is highly unlikely that these campaigns are completely homegrown. In this direction, some past research has pointed towards black markets for reusable political disinformation bots [19]. We believe that it is important for future research to look at disinformation and influence campaigns through a larger lens. ...
Preprint
Full-text available
Social media platforms offer unprecedented opportunities for connectivity and exchange of ideas; however, they also serve as fertile grounds for the dissemination of disinformation. Over the years, there has been a rise in state-sponsored campaigns aiming to spread disinformation and sway public opinion on sensitive topics through designated accounts, known as troll accounts. Past works on detecting accounts belonging to state-backed operations focus on a single campaign. While campaign-specific detection techniques are easier to build, there is no work done on developing systems that are campaign-agnostic and offer generalized detection of troll accounts unaffected by the biases of the specific campaign they belong to. In this paper, we identify several strategies adopted across different state actors and present a system that leverages them to detect accounts from previously unseen campaigns. We study 19 state-sponsored disinformation campaigns that took place on Twitter, originating from various countries. The strategies include sending automated messages through popular scheduling services, retweeting and sharing selective content and using fake versions of verified applications for pushing content. By translating these traits into a feature set, we build a machine learning-based classifier that can correctly identify up to 94% of accounts from unseen campaigns. Additionally, we run our system in the wild and find more accounts that could potentially belong to state-backed operations. We also present case studies to highlight the similarity between the accounts found by our system and those identified by Twitter.
... Several works of author profiling have been done on a single and specific perspective such as organization and individual classification [3,6,7], political orientation [8], bot detection [9], age prediction [10]. Twitter user classification approaches involves three major types of research, namely statistical-based approaches, content-based approaches and hybrid approaches. ...
... Twitter user classification approaches involves three major types of research, namely statistical-based approaches, content-based approaches and hybrid approaches. First, statistical-based approaches that used statistical parameters such as the metadata of user profiles [9], the time distribution between posts [11], post frequency [12]. Second, content-based approaches that used only the textual features from (NLP) such as n-grams [2], n-grams, linguistics, informal language and twitter stylistic features [7], word2vec neural language model with a Convolutional Neural Network (DCCN) [10], the semantic Long Short Term Memory (LSTM) [13]. ...
Conference Paper
Full-text available
This paper presents a novel technique for classifying user accounts on Twitter. The main purpose of our classification is to distinguish the patterns of users from those of individuals and organizations. However, such a task is non-trivial. Classic and consolidated approaches use textual features from Natural Language Processing (NLP) for classification. Nevertheless, such approaches still have some drawbacks like the computational cost and the fact that they depend on a specific language. In this work, we propose a statistical-based approach based on metadata of user profiles, popularity of posts and other statistical features in order to recognize the type of users without using the textual content. We performed a set of experiments over a twitter dataset and learn-based algorithms. This yielded an F-measure of 95.6% using the Random Forest algorithm and synthetic minority oversampling technique.
... We use two Twitter datasets: a COVID-19 corpus (Chen et al. 2020) and a 2017 French Election corpus (Ferrara 2017). They cover different topics (politics and pandemic) and two languages (primarily in English and in French, respectively). ...
... 2017 French Election Corpus This corpus includes keyword-identified tweets related to the 2017 French presidential Election (Ferrara 2017), and covers the period from April 26 to May 29, 2017. We focus on original tweets written in top three languages in the corpus-French, English, and Spanish, totaling in 2,438K tweets. ...
Article
Narratives are foundation of human cognition and decision making. Because narratives play a crucial role in societal discourses and spread of misinformation and because of the pervasive use of social media, the narrative dynamics on social media can have profound societal impact. Yet, systematic and computational understanding of online narratives faces critical challenge of the scale and dynamics; how can we reliably and automatically extract narratives from massive amount of texts? How do narratives emerge, spread, and die? Here, we propose a systematic narrative discovery framework that fill this gap by combining change point detection, semantic role labeling (SRL), and automatic aggregation of narrative fragments into narrative networks. We evaluate our model with synthetic and empirical data — two Twitter corpora about COVID-19 and 2017 French Election. Results demonstrate that our approach can recover major narrative shifts that correspond to the major events.
... Las evidencias sobre campañas de desinformación también han aparecido en otros procesos políticos relevantes de los últimos años. Por ejemplo, en 2016 con motivo del referéndum en el Reino Unido (Bastos y Mercea, 2019), en las elecciones presidenciales de Francia de 2017 (Ferrara, 2017), o durante las elecciones generales de Brasil de 2018, en las que resultó elegido Jair Bolsonaro (Recuero et al., 2020). En España, el proceso independentista en Cataluña también ha sido un caso especialmente fecundo en mensajes desinformativos (Aparici et al., 2019), con sospechas de injerencias por parte de países extranjeros, particularmente de Rusia (Schwirtz y Bautista, 2021). ...
... 24 Disinformation campaigns increasingly cloud public discussions, especially during important times like parliamentary elections. 25 The misuse of information can lead to a distorted public perception creating an uneven playing field in political contests, potentially swaying elections. 26 By sowing doubt and confusion, misinformation jeopardises the free exchange of ideas and undermine the right to freedom of information which are essential for a functioning democracy. ...
Article
Full-text available
Social media networks have become predominant sources of free and open access information. However, they generally fall outside the scope of media regulation, leaving information on those platforms largely unregulated. While social media is a catalyst for disinformation and propaganda, it also allows for fast and widespread dissemination of reliable information. A crisis response mechanism (CRM) was added to the Digital Services Act which gives the European Commission the power to require providers of very large online platforms to assess the contribution of their services to a serious threat to public security or public health and apply effective countermeasures. Although the CRM interferes with a social media network’s right to conduct business it is necessary to safeguard individuals’ right to reliable information which is essential to democracy.
... Previous work in social media analysis, especially within the area of social news, has focused on influence campaigns and the spread of (mis)information either organically or through bots. Given a particular event like an election or a natural disaster, researchers typically follow information cascades to tease out diffusion processes and infer various characteristics about how social media responded to the event [2], [3]. These studies have resulted in important findings about the effect of such items as information contagion [4], influence campaigns [5], bots [6], and spam [7], etc., within specific newsworthy events. ...
Preprint
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As people rely on social media as their primary sources of news, the spread of misinformation has become a significant concern. In this large-scale study of news in social media we analyze eleven million posts and investigate propagation behavior of users that directly interact with news accounts identified as spreading trusted versus malicious content. Unlike previous work, which looks at specific rumors, topics, or events, we consider all content propagated by various news sources. Moreover, we analyze and contrast population versus sub-population behaviour (by demographics) when spreading misinformation, and distinguish between two types of propagation, i.e., direct retweets and mentions. Our evaluation examines how evenly, how many, how quickly, and which users propagate content from various types of news sources on Twitter. Our analysis has identified several key differences in propagation behavior from trusted versus suspicious news sources. These include high inequity in the diffusion rate based on the source of disinformation, with a small group of highly active users responsible for the majority of disinformation spread overall and within each demographic. Analysis by demographics showed that users with lower annual income and education share more from disinformation sources compared to their counterparts. News content is shared significantly more quickly from trusted, conspiracy, and disinformation sources compared to clickbait and propaganda. Older users propagate news from trusted sources more quickly than younger users, but they share from suspicious sources after longer delays. Finally, users who interact with clickbait and conspiracy sources are likely to share from propaganda accounts, but not the other way around.
... They found that luddites greatly limit adoption if the adoption rate is high but not if it is low. Gambaro and Crokidakis [60] illustrated that contrarian agents can be a source of disorder in opinion dynamics, and Ferrara and collaborators have investigated how individual social-media accounts controlled by bots can exert a considerable influence on political elections and social cascades [17,61,62]. ...
Preprint
The spread of opinions, memes, diseases, and "alternative facts" in a population depends both on the details of the spreading process and on the structure of the social and communication networks on which they spread. In this paper, we explore how \textit{anti-establishment} nodes (e.g., \textit{hipsters}) influence the spreading dynamics of two competing products. We consider a model in which spreading follows a deterministic rule for updating node states (which describe which product has been adopted) in which an adjustable fraction pHipp_{\rm Hip} of the nodes in a network are hipsters, who choose to adopt the product that they believe is the less popular of the two. The remaining nodes are conformists, who choose which product to adopt by considering which products their immediate neighbors have adopted. We simulate our model on both synthetic and real networks, and we show that the hipsters have a major effect on the final fraction of people who adopt each product: even when only one of the two products exists at the beginning of the simulations, a very small fraction of hipsters in a network can still cause the other product to eventually become the more popular one. To account for this behavior, we construct an approximation for the steady-state adoption fraction on k-regular trees in the limit of few hipsters. Additionally, our simulations demonstrate that a time delay τ\tau in the knowledge of the product distribution in a population, as compared to immediate knowledge of product adoption among nearest neighbors, can have a large effect on the final distribution of product adoptions. Our simple model and analysis may help shed light on the road to success for anti-establishment choices in elections, as such success can arise rather generically in our model from a small number of anti-establishment individuals and ordinary processes of social influence on normal individuals.
... Despite their rudimentary social behaviour and weak network integration, Twitter bots substantially influence political communication, public opinion, elections and markets. They have had an important role in misinformation dissemination in relation to political events [206][207][208][209][210][211][212]214) and stock market investment 215 . Bots can affect human interaction networks by encouraging followings and conversations 216 and amplify low-credibility content early on by targeting influential humans through replies and mentions 210 . ...
Article
From fake social media accounts and generative artificial intelligence chatbots to trading algorithms and self-driving vehicles, robots, bots and algorithms are proliferating and permeating our communication channels, social interactions, economic transactions and transportation arteries. Networks of multiple interdependent and interacting humans and intelligent machines constitute complex social systems for which the collective outcomes cannot be deduced from either human or machine behaviour alone. Under this paradigm, we review recent research and identify general dynamics and patterns in situations of competition, coordination, cooperation, contagion and collective decision-making, with context-rich examples from high-frequency trading markets, a social media platform, an open collaboration community and a discussion forum. To ensure more robust and resilient human-machine communities, we require a new sociology of humans and machines. Researchers should study these communities using complex system methods; engineers should explicitly design artificial intelligence for human-machine and machine-machine interactions; and regulators should govern the ecological diversity and social co-development of humans and machines.
... Social bots spread specific messages by posting many tweets with similar content on social networks [60]. Other works [11,61,62] consider substantial meta users and features in the elaborate machine learning models particularly for the consideration of scalability and generalization; however, they usually come with non-negligible time and computational cost. There is also a huge body of detection approaches based on deep learning techniques. ...
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Detecting anomalous users in social networks is an imperative but challenging task. The increasing complexity of inter-personal behaviors and interactions further complicates the development of effective user anomaly detection techniques. Current state-of-the-art methods heavily rely on static personal features, making it difficult to quantify the hidden relevance of user behaviors through traditional feature engineering. This loss of accuracy is exacerbated by the rise of sophisticated camouflage and disguising techniques, which blur the distinction between anomalous and regular users. In this paper, we present GNNRI, an innovative framework for detecting anomalous users in social networks. Our approach leverages a network representation learning model and a heterogeneous information network (Hin) to explore hidden semantic connections from user metadata, tweets, and interaction information. We extract both user metadata and behavioral features to construct a Hin and introduce two distinct learning layers to explore explicit and implicit user relevance. First, we employ a relation-based self-attention layer to aggregate neighbor node closeness under specific relations and across different relationships. Subsequently, we apply graph convolution network-based convolutional learning layers, which enhance embedding effectiveness by capturing graph-wide node similarity. We evaluate GNNRI using real-world datasets, and our results demonstrate that it outperforms all other comparative baselines, achieving approximately 90% accuracy for user classification, with a 5–15% improvement over other GNN variants. Notably, even when using only 20% of the data for training, GNNRI achieves 87.8%, 86.57%, and 87.1% accuracy for detecting zombies, spammers, and bots, respectively.
... In a similar manner, with advancements in AI, bots are increasingly integrated into daily life, functioning as committed agents due to their controllability and scalability. For example, on platforms like Twitter, bots have been used to influence public opinion [7,8] and even affect elections [9]. While ethical concerns surround their use, AI introduces new possibilities for shaping social dynamics, transcending traditional human limitations [10,11]. ...
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The rise of artificial intelligence (AI) offers new opportunities to influence cooperative dynamics with greater applicability and control. In this paper, we examine the impact of third-party bots--agents that do not directly participate in games but unbiasedly modify the payoffs of normal players engaged in prisoner's dilemma interactions--on the emergence of cooperation. Using an evolutionary simulation model, we demonstrate that unbiased bots are unable to shift the defective equilibrium among normal players in well-mixed populations. However, in structured populations, despite their unbiased actions, the bots spontaneously generate distinct impacts on cooperators and defectors, leading to enhanced cooperation. Notably, bots that apply negative influences are more effective at promoting cooperation than those applying positive ones, as fewer bots are needed to catalyze cooperative behavior among normal players. However, as the number of bots increases, a trade-off emerges: while cooperation is maintained, overall social payoffs decline. These findings highlight the need for careful management of AI's role in social systems, as even well-intentioned bots can have unintended consequences on collective outcomes.
... The bulk of the literature has emphasized the antecedents of conspiratorial theorizing and its pernicious social consequences. In political science there is strong interest in the effect of conspiracy theories and other forms of false information on elections and referenda (Ferrara 2017, Jolley et al. 2022. In medicine, there is research on the effect of conspiracy theories on selfand other-protecting behavior (Allington et al. 2021, Romer & Jamieson 2020. ...
Article
Conspiracy theories are a constant feature of human society but have recently risen in prominence with the flurry of COVID-19 conspiracy theories and their public display in social media. Conspiracy theories should be studied not only because of their potential harm but also because they are related to other sources of misinformation such as folk theories, rumors, and fake news. Recent understanding of their spread has shifted the focus from investigating the believers to characteristics of the social processes that motivate and persuade, with a new view of the conspiracy theorist as a bricoleur dealing with threats through social (re)construction of reality. These tendencies are strengthened by the markets for attention and approval constructed by social media platforms, and bots also amplify them. We identify an agenda of multiple important and urgent paths for future research that will help understanding of conspiracy theories in society.
... Misinformation campaigns on the Internet have affected several countries in recent years, endangering the integrity of public discourse, electoral processes, and democratic governance (Allcott and Gentzkow 2017;Spohr 2017;Ferrara 2017;Lazer et al. 2018). In the Coronavirus pandemic, for instance, the issue has reached new levels, including the diminishment of public health concerns, promotion of medication without proven efficacy, or dismissal of sanitary measures (van Der Linden et al. 2020;Depoux et al. 2020). ...
Article
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Misinformation has become a global issue with impacts on various spheres of society, particularly in developing countries like Brazil. In most misinformation ecosystems, a recurring challenge is the spread of fake news through websites that closely replicate the look and function of reputable news outlets. This facilitates their dissemination, which might also involve automation, political bias, targeted ads and even the exploitation of social network algorithms in an attempt to reach niche audiences. Due to this high complexity and the rapidly evolving nature of the problem, we are just beginning to identify patterns in the various misinformation ecosystems on the Web. In this work, we extend a previous study, offering important steps towards a deeper understanding of computer networking patterns observed on Brazilian misinformation websites. These patterns emerge from various sources, including DNS records, domain registrations, TLS certificates and hosting infrastructure. Our results reveal a novel avenue through which low-credibility news websites can be distinguished from websites of credible nature.
... However, it was later found that despite the large number of tweets and the timing of their release, the campaign had had little impact (Vilmer, 2019). Users who engaged with the campaign were found to be mainly foreign and not French (Ferrara, 2017). Lemke and Habegger (2022) studied the Macron Leaks by analyzing the RT and Sputnik Twitter feeds and independent accounts (so-called panel tweets) that were active during and after the presidential election. ...
Chapter
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This chapter seeks to explain why disinformation should be seen as an everyday practice and how this is connected to digital news media. It takes its point of departure in the notion that the digital media ecology enables the construction and dissemination of strategic narratives (Oates, 2014) and that media technological developments have expanded the digital news media market and its audiences globally. At the same time, these organizational, technological, and political media developments are being used increasingly successfully by authoritarian regimes to suppress, control, and surveille. Sweden is one of the Western countries that has been heavily targeted by Russian disinformation by way of international news. RT and Sputnik news coverage tends to reflect Russian perceptions of the West as a threat to the values that the Kremlin has declared it will defend. They can be summarized as the promotion of typical national conservative values such as the promotion of Orthodox Christianity, “morality”, and “traditional family values”. More importantly, these Russian state media center their coverage on explaining the decline of Western states as a consequence of the increase of Islam in politics and society, LGBTI+communities, and migration. The chapter reviews studies about how RT and Sputnik construct denigrating depictions of a number of different countries attempting to use such imagery to sow distrust between citizens and politicians, and between citizens and public institutions both on a day-to-day basis and centered on events such as election campaigns or foreign policy crisis.
... With the rise of social media and its paramount role in information production, one of the key objectives of information verification is to determine if the information derives from an authentic source or has been manipulated by a malicious actor. For example, Company 4, which was launched in 2014, uses machine learning to determine if a specific Twitter user is authentic or is a "social bot" i.e., an account pretending to be a genuine human user that is actually controlled by computational means (Ferrara, 2017). As Company 4's website mentions, "social bots can be used to manipulate social media users by amplifying misinformation". ...
Chapter
As the scale of mis/disinformation grows across media and social media platforms, public and professional discussion about the use of artificial intelligence (AI) for information verification is becoming increasingly common. This chapter explores how six companies working on AI-powered services strategically frame mis/disinformation issues and what sort of moral judgments they use when making diagnostic inferences to find solutions for “information disorder”. Informed by Entman’s framing theory, this study qualitatively analyzes textual data from the websites of AI-powered services for information verification. We find that the companies studied promote services that we identify here as: automated fact-checking, automated credibility assessment, and automated authenticity assessment. Hence, this chapter focuses on the strategic framing of the mis/disinformation problem, along with the solutions promoted by AI-powered services, laying the groundwork for further explorations of how the offered technologies might tackle the problem of spreading fake news.
... This shift has reshaped the information landscape, presenting both opportunities and challenges. A primary concern is the potential rapid dissemination of misinformation and its far-reaching impact on various aspects of society, spanning from the realm of politics [1][2][3][4][5][6], to critical societal issues like climate change [7] and vaccines [8][9][10]. The presence of misinformation on social media has been acknowledged as a phenomenon with the potential to influence the outcomes of crucial societal processes, leading scholars to increasingly focus on addressing this issue. ...
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The Internet and social media have transformed the information landscape, democratizing content access and production. While making information easily accessible, these platforms can also act as channels for spreading misinformation, posing crucial societal challenges. To address this, understanding news consumption patterns and unraveling the complexities of the online information environment are essential. Previous studies highlight polarization and misinformation in online discussions, but many focus on specific topics or contexts, often overlooking comprehensive cross-country and cross-topic analyses. However, the dynamics of debates, misinformation prevalence, and the efficacy of countermeasures are intrinsically tied to socio-cultural contexts. This work aims to bridge this gap by exploring information consumption patterns across four European countries over three years. Analyzing the Twitter activity of news outlets in France, Germany, Italy, and the UK, this study seeks to shed light on how topics of European significance resonate across these nations and the role played by misinformation sources. The results spotlight that while reliable sources predominantly shape the information landscape, unreliable content persists across all countries and topics. Though most users favor trustworthy sources, a small percentage predominantly consumes content from questionable sources, with even fewer maintaining a mixed information diet. The cross-country comparison unravels disparities in audience overlap among news sources, the prevalence of misinformation, and the proportion of users relying on questionable sources. Such distinctions surface not only across countries but also within various topics. These insights underscore the pressing need for tailored studies, crucial in designing targeted and effective countermeasures against misinformation and extreme polarization in the digital space.
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Przedmiotem badań, których wyniki opisane zostały w niniejszej monografii, jest kwestia nowych technologii jako instrumentu realizacji polityki bezpieczeństwa informacyjnego. Celem badań uczyniono analizę aktualnego stanu wykorzystania nowoczesnych technologii w procesie zapewniania i podnoszenia poziomu bezpieczeństwa, z naciskiem na jego informacyjny wymiar. Ogólny problem badawczy przyjął postać następującego pytania: W jaki sposób wykorzystuje się nowe technologie do realizacji polityki bezpieczeństwa informacyjnego? Punktem wyjścia do analizy tak sformułowanego problemu stała się reaktywna natura bezpieczeństwa wobec zagrożeń, na które stanowi ono odpowiedź. Przyjęto, iż szeroką, ale i interesującą perspektywę dla takiego badania, daje złożona przestrzeń walki informacyjnej. Następnie dokonano wyboru trzech nowych technologii do studiów przypadku. Mając pełną świadomość ich synergii i komplementarności, kierowano się kryterium powszechności, potencjału oddziaływania i bezpośredniości dostępu. Zdecydowano się na sztuczną inteligencję, Internet rzeczy oraz jego rozwinięcie w postaci smart city. Uznano, iż pozwalają one na precyzyjniejsze ukazanie kwestii bezpieczeństwa informacyjnego i potrzeby świadomego kreowania jego polityki.
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The COVID-19 pandemic has given rise to numerous conspiracy theories, focusing on topics such as vaccine effectiveness, the virus’s origin and public health measures. These narratives have spread rapidly through social and traditional media, influencing public perception and behaviour towards vaccination efforts. In particular, they have contributed significantly to vaccine hesitancy. This study focuses on Italy, where narratives linking COVID-19 vaccinations to sudden deaths have gained significant traction. By conducting a thorough investigation into post-vaccination mortality rates, this study aims to shed light on the true nature of the data regarding deaths and mortality rates in Italy. The study uses a Difference-In-Differences (DID) framework to analyse post-vaccination mortality rates across Italian municipalities from 2018 to 2023. Results indicate that the overall mortality rate did not significantly increase following the vaccination campaign, and the impact varied across different demographic groups and regions, indicating disparities in healthcare delivery, public health strategies and demographic factors between the North and the South. This study contributes innovatively by providing empirical evidence from Italy, addressing a critical gap in understanding the relationship between vaccination and public health outcomes.
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Drawing on a critical review of the existing literature on computational propaganda and disinformation, and employing a three-stage process—addressing the “New Wine in Old Bottles” problem, extracting foundational concepts, and constructing a four-pillar framework—this article proposes an expanded theory of propaganda. The theory posits that digital propaganda is shaped by four key dimensions: politico-economic, sociocultural, technological, and socio-psychological, further delineated by the forces of commodification, privatization, connectivity, and virality. Broadening the analytical scope, it encompasses intricate interactions among politics, content, actors, platforms, and goals, recognizing the dynamic complexities inherent in the digital landscape. Furthermore, it sheds light on how commercial interests impact the production and dissemination of propaganda, offering insights into the propagation of popular ideologies such as patriotism and populism. This advances the understanding of digital propaganda’s pervasive impact on political discourse and societal attitudes, encouraging broader global research beyond a focus on state actors.
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The share of social media attention to political candidates was shown to be a good predictor of election outcomes in several studies. This attention to individual candidates fluctuates due to incoming daily news and sometimes reflects long-term trends. By analyzing Twitter data in the 2013 and 2022 election campaign we observe that, on short timescales, the dynamics can be effectively characterized by a mean-reverting diffusion process on a logarithmic scale. This implies that the response to news and the exchange of opinions on Twitter lead to attention fluctuations spanning orders of magnitudes. However, these fluctuations remain centered around certain average levels of popularity, which change slowly in contrast to the rapid daily and hourly variations driven by Twitter trends and news. In particular, on our 2013 data we are able to estimate the dominant timescale of fluctuations at around three hours. Finally, by considering the extreme data points in the tail of the attention variation distribution, we could identify critical events in the electoral campaign period and extract useful information from the flow of data.
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The proliferation of disinformation has become an issue in recent years due to the widespread use of social media. This study analyzes the recent activities of social bots in Japan, which may be used for the purpose of spreading disinformation, and clarifies the characteristics of influential social bots. Specifically, we collected data from X (formerly Twitter) on several news items that had a great impact in Japan, and analyzed the spread of information by social bots using a combination of existing tools and methods. Our analysis compared the number and percentage of social bots on X in Japanese cases to existing research analyzing those during the 2016 US presidential election, and clarified what kind of social bots are influencing the information diffusion. In all cases we examined, our analysis showed that social bot activity in Japan was more active than during the 2016 US presidential election. We also found that humans are spreading posts created by social bots, as was the case during the 2016 US presidential election. Furthermore, we confirmed that the characteristics of social bots reposted by humans on X in Japan are similar to human accounts, and it is difficult to detect them using only the profile information on the X account page.
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Mounting critique of the way AI is framed in mainstream media calls for less sensationalist coverage, be it jubilant or apocalyptic, and more attention to the concrete situations in which AI becomes controversial in different ways. This is supposedly achieved by making coverage more expert-informed. We therefore explore how experts contribute to the issuefication of AI through the scientific literature. We provide a semantic, visual network analysis of a corpus of 1M scientific abstracts about machine learning algorithms and artificial intelligence. Through a systematic quali-quantitative exploration of 235 co-word clusters and a subsequent structured search for 18 issue-specific queries, for which we devise a novel method with a custom-built datascape, we explore how algorithms have agency. We find that scientific discourse is highly situated and rarely about AI in general. It overwhelmingly charges algorithms with the capacity to solve problems and these problems are rarely about algorithms in their origin. Conversely, it rarely charges algorithms with the capacity to cause problems and when it does, other algorithms are typically charged with the capacity to solve them. Based on these findings, we argue that while a more expert-informed coverage of AI is likely to be less sensationalist and show greater attention to the specific situations where algorithms make a difference, it is unlikely to stage AI as particularly controversial. Consequently, we suggest conceptualising AI as a political situation rather than something inherently controversial.
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Online disinformation, having disruptive impacts on democracy and fundamental rights and freedoms, is one of the most important problems to be tackled in today's information ecosystem. However, it is highly possible that the regulatory responses to be given to tackle it will result in unlawful interference with the freedom of expression. The study maps out how online disinformation can be tackled without such interference. In doing so, it primarily interprets Article 10 of the ECHR and the case law of the ECtHR on the subject. The regulatory responses of the UN, EU, national regulations, and social media platforms are also referred to the extent required by the research. Tackling online disinformation should prioritize resolving the structural problems of the information ecosystem and building a secure one. In the case of legal regulations limiting freedom of expression, it should be ensured that these regulations are prescribed by law, pursue a legitimate aim, and are necessary in a democratic society.
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Social media, seen by some as the modern public square, is vulnerable to manipulation. By controlling inauthentic accounts impersonating humans, malicious actors can amplify disinformation within target communities. The consequences of such operations are difficult to evaluate due to the challenges posed by collecting data and carrying out ethical experiments that would influence online communities. Here we use a social media model that simulates information diffusion in an empirical network to quantify the impacts of adversarial manipulation tactics on the quality of content. We find that the presence of hub accounts, a hallmark of social media, exacerbates the vulnerabilities of online communities to manipulation. Among the explored tactics that bad actors can employ, infiltrating a community is the most likely to make low-quality content go viral. Such harm can be further compounded by inauthentic agents flooding the network with low-quality, yet appealing content, but is mitigated when bad actors focus on specific targets, such as influential or vulnerable individuals. These insights suggest countermeasures that platforms could employ to increase the resilience of social media users to manipulation.
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With the rise and prevalence of social bots, their negative impacts on society are gradually recognized, prompting research attention to effective detection and countermeasures. Recently, graph neural networks (GNNs) have flourished and have been applied to social bot detection research, improving the performance of detection methods effectively. However, existing GNN-based social bot detection methods often fail to account for the heterogeneous associations among users within social media contexts, especially the heterogeneous integration of social bots into human communities within the network. To address this challenge, we propose a heterogeneous compatibility perspective for social bot detection, in which we preserve more detailed information about the varying associations between neighbors in social media contexts. Subsequently, we develop a compatibility-aware graph neural network (CGNN) for social bot detection. CGNN consists of an efficient feature processing module, and a lightweight compatibility-aware GNN encoder, which enhances the model’s capacity to depict heterogeneous neighbor relations by emulating the heterogeneous compatibility function. Through extensive experiments, we showed that our CGNN outperforms the existing state-of-the-art (SOTA) method on three commonly used social bot detection benchmarks while utilizing only about 2% of the parameter size and 10% of the training time compared with the SOTA method. Finally, further experimental analysis indicates that CGNN can identify different edge categories to a significant extent. These findings, along with the ablation study, provide strong evidence supporting the enhancement of GNN’s capacity to depict heterogeneous neighbor associations on social media bot detection tasks.
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Online social networks are easily exploited by social bots. Although the current models for detecting social bots show promising results, they mainly rely on Graph Neural Networks (GNNs), which have been proven to have vulnerabilities in robustness and these detection models likely have similar robustness vulnerabilities. Therefore, it is crucial to evaluate and improve their robustness. This paper proposes a robustness evaluation method: Attribute Random Iteration-Fast Gradient Sign Method (ARI-FGSM) and uses a simplified adversarial training to improve the robustness of social bot detection. Specifically, this study performs robustness evaluations of five bot detection models on two datasets under both black-box and white-box scenarios. The white-box experiments achieve a minimum attack success rate of 86.23%, while the black-box experiments achieve a minimum attack success rate of 45.86%. This shows that the social bot detection model is vulnerable to adversarial attacks. Moreover, after executing our robustness improvement method, the robustness of the detection model increased by up to 86.98%.
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Plain Language Summary This paper focuses on exploring whether social robots with strong algorithm-based support can also participate in agenda-setting. This paper studies the content of social media discussion of the presidential election in South Korea, determines the degree of participation of social robots, and explores the connection between the media agenda, robot agenda, and public agenda from the perspective of agenda setting. This study used the following data and methods. First, tweets related to the 2022 South Korean presidential elections were the material used for analysis. Second, we used Botometer to identify social bots and divided all accounts into media accounts, social bots, and human users. Third, cluster analysis was used to cluster the tweets and the agendas included in the discussion of the 2022 South Korean presidential elections would be determined. The manual and computer-aided topic coding was used to determine the distribution of the agenda in the media, bots, and public accounts. Fourth, we analyzed the correlation between media agenda, bot agenda, and public agenda using SPSS software. Fifth, we used Almon Polynomial Lag to identify the time lag effect. Through the result of time lag, whose agenda was ahead in time would be judged. The results show that while the main agendas of the media, social bots, and the public are not the same, their agendas are related. Moreover, the media agenda is not ahead of the robot agenda and the public agenda in time, and the chronological order only appears between social robots and the public.
Preprint
Social media bot detection is increasingly crucial with the rise of social media platforms. Existing methods predominantly construct social networks as graph and utilize graph neural networks (GNNs) for bot detection. However, most of these methods focus on how to improve the performance of GNNs while neglecting the community structure within social networks. Moreover, GNNs based methods still face problems such as poor model generalization due to the relatively small scale of the dataset and over-smoothness caused by information propagation mechanism. To address these problems, we propose a Community-Aware Heterogeneous Graph Contrastive Learning framework (CACL), which constructs social network as heterogeneous graph with multiple node types and edge types, and then utilizes community-aware module to dynamically mine both hard positive samples and hard negative samples for supervised graph contrastive learning with adaptive graph enhancement algorithms. Extensive experiments demonstrate that our framework addresses the previously mentioned challenges and outperforms competitive baselines on three social media bot benchmarks.
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This paper introduces a novel approach for the detection of automated entities in online environments through the analysis of mouse dynamics. Leveraging fractional derivatives and vector cross products, our methodology scrutinizes the intricate patterns embedded in mouse movements. Fractional derivatives capture the non-integer order dynamics, while vector cross products reveal deviations from expected trajectories. The combination of these advanced mathematical tools offers a unique perspective on distinguishing between human and bot behaviors. We present experimental results showcasing the efficacy of our approach in various scenarios, demonstrating its potential in the realm of cybersecurity and online integrity. Our findings contribute to the evolving landscape of bot detection methodologies, emphasizing the importance of incorporating mathematical rigor in the analysis of digital behavior.
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Conference Paper
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While most online social media accounts are controlled by humans, these platforms also host automated agents called social bots or sybil accounts. Recent literature reported on cases of social bots imitating humans to manipulate discussions, alter the popularity of users, pollute content and spread misinformation, and even perform terrorist propaganda and recruitment actions. Here we present BotOrNot, a publicly-available service that leverages more than one thousand features to evaluate the extent to which a Twitter account exhibits similarity to the known characteristics of social bots. Since its release in May 2014, BotOrNot has served over one million requests via our website and APIs.
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Recent studies in social media spam and automation provide anecdotal argumentation of the rise of a new generation of spambots, so-called social spambots. Here, for the first time, we extensively study this novel phenomenon on Twitter and we provide quantitative evidence that a paradigm-shift exists in spambot design. First, we measure current Twitter's capabilities of detecting the new social spambots. Later, we assess the human performance in discriminating between genuine accounts, social spambots, and traditional spambots. Then, we benchmark several state-of-the-art techniques proposed by the academic literature. Results show that neither Twitter, nor humans, nor cutting-edge applications are currently capable of accurately detecting the new social spambots. Our results call for new approaches capable of turning the tide in the fight against this raising phenomenon. We conclude by reviewing the latest literature on spambots detection and we highlight an emerging common research trend based on the analysis of collective behaviors. Insights derived from both our extensive experimental campaign and survey shed light on the most promising directions of research and lay the foundations for the arms race against the novel social spambots. Finally, to foster research on this novel phenomenon, we make publicly available to the scientific community all the datasets used in this study.
Conference Paper
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Social bots can affect online communication among humans. We study this phenomenon by focusing on #YaMeCanse, the most active protest hashtag in the history of Twitter in Mexico. Accounts using the hashtag are classified using the BotOrNot bot detection tool. Our preliminary analysis suggests that bots played a critical role in disrupting online communication about the protest movement.
Conference Paper
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The WWW has seen a massive growth in variety and usage of OSNs. The rising population of users on Twitter and its open nature has made it an ideal platform for various kinds of opportunistic pursuits, such as news and emergency communication, business promotion, political campaigning, spamming and spreading malicious content. Most of these opportunistic pursuits are exploited through automated programs, known as bots. In this study we propose a framework (Stweeler) to study bot impact and influence on Twitter from systems and social media perspectives.
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From politicians and nation states to terrorist groups, numerous organizations reportedly conduct explicit campaigns to influence opinions on social media, posing a risk to freedom of expression. Thus, there is a need to identify and eliminate "influence bots"--realistic, automated identities that illicitly shape discussions on sites like Twitter and Facebook--before they get too influential.
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Significance The wide availability of user-provided content in online social media facilitates the aggregation of people around common interests, worldviews, and narratives. However, the World Wide Web is a fruitful environment for the massive diffusion of unverified rumors. In this work, using a massive quantitative analysis of Facebook, we show that information related to distinct narratives––conspiracy theories and scientific news––generates homogeneous and polarized communities (i.e., echo chambers) having similar information consumption patterns. Then, we derive a data-driven percolation model of rumor spreading that demonstrates that homogeneity and polarization are the main determinants for predicting cascades’ size.
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Egypt's netizens succeeded in mobilizing for the Revolution of 25 January using social media. The revolution which started as an event on the social networking site Facebook.com took the world by storm when Egyptians succeeded in overthrowing a dictator who ruled the country for almost three decades. For the past few years in Egypt, social media became a powerful tool used by citizens to uncover corruption, mobilize for protests, and act as real watchdog over the mainstream media and the government. Although social media have mostly been used by citizens as a platform for public opinion expression and mobilization, they have become important propaganda tools used by governments. In the case of Egypt, the Supreme Council of the Armed Forces (SCAF) which ruled Egypt for a transitional period of 16 months after Mubarak stepped down, realized the need to speak the same language of the Egyptian youth, to communicate with them electronically, as well as to issue counter-revolutionary propaganda. This paper will mainly focus on SCAF's propaganda on the social networking Web site Facebook and the different propaganda techniques used in post-revolutionary Egypt.
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Systems that classify influential users in social networks have been used frequently and are referenced in scientific papers and in the media as an ideal standard of evaluation of influence in the Twitter social network. We consider such systems of evaluation to be complex and subjective, and we therefore suspect that they are vulnerable and easy to manipulate. Based on this, we performed experiments and analysis of two systems for ranking influence: Klout and Twitalyzer. We created simple robots capable of interacting by means of Twitter accounts, and we measured how influent they were. Our results show that it is possible to become influential through simple strategies. This suggests that the systems do not have ideal means to measure and classify influence.
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Social media are used as main discussion channels by millions of individuals every day. The content individuals produce in daily social-media-based micro-communications, and the emotions therein expressed, may impact the emotional states of others. A recent experiment performed on Facebook hypothesized that emotions spread online, even in absence of non-verbal cues typical of in-person interactions, and that individuals are more likely to adopt positive or negative emotions if these are over-expressed in their social network. Experiments of this type, however, raise ethical concerns, as they require massive-scale content manipulation with unknown consequences for the individuals therein involved. Here, we study the dynamics of emotional contagion using Twitter. Rather than manipulating content, we devise a null model that discounts some confounding factors (including the effect of emotional contagion). We measure the emotional valence of content the users are exposed to before posting their own tweets. We determine that on average a negative post follows an over-exposure to 4.34% more negative content than baseline, while positive posts occur after an average over-exposure to 4.50% more positive contents. We highlight the presence of a linear relationship between the average emotional valence of the stimuli users are exposed to, and that of the responses they produce. We also identify two different classes of individuals: highly and scarcely susceptible to emotional contagion. Highly susceptible users are significantly less inclined to adopt negative emotions than the scarcely susceptible ones, but equally likely to adopt positive emotions. In general, the likelihood of adopting positive emotions is much greater than that of negative emotions.
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Over the last several years political actors worldwide have begun harnessing the digital power of social bots - software programs designed to mimic human social media users on platforms like Facebook, Twitter, and Reddit. Increasingly, politicians, militaries, and government-contracted firms use these automated actors in online attempts to manipulate public opinion and disrupt organizational communication. Politicized social bots - here 'political bots' - are used to massively boost politicians' follower levels on social media sites in attempts to generate false impressions of popularity. They are programmed to actively and automatically flood news streams with spam during political crises, elections, and conflicts in order to interrupt the efforts of activists and political dissidents who publicize and organize online. They are used by regimes to send out sophisticated computational propaganda. This paper conducts a content analysis of available media articles on political bots in order to build an event dataset of global political bot deployment that codes for usage, capability, and history. This information is then analyzed, generating a global outline of this phenomenon. This outline seeks to explain the variety of political bot-oriented strategies and presents details crucial to building understandings of these automated software actors in the humanities, social and computer sciences.
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The political campaign is one of the most important organizations in a democracy, and whether issue, or candidate, specific, it is one of the least understood organizations in contemporary political life. With evidence from ethnographic immersion, survey data, and social network analysis, Philip Howard examines the evolving act of political campaigning and the changing organization of political campaigns over the last five election cycles, from 1996 to 2004. Over this time, both grassroots and elite political campaigns have gone online, built multimedia strategies, and constructed complex relational databases.
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Social and political bots have a small but strategic role in Venezuelan political conversations. These automated scripts generate content through social media platforms and then interact with people. In this preliminary study on the use of political bots in Venezuela, we analyze the tweeting, following and retweeting patterns for the accounts of prominent Venezuelan politicians and prominent Venezuelan bots. We find that bots generate a very small proportion of all the traffic about political life in Venezuela. Bots are used to retweet content from Venezuelan politicians but the effect is subtle in that less than 10 percent of all retweets come from bot-related platforms. Nonetheless, we find that the most active bots are those used by Venezuela's radical opposition. Bots are pretending to be political leaders, government agencies and political parties more than citizens. Finally, bots are promoting innocuous political events more than attacking opponents or spreading misinformation.
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The computer science research community has became increasingly interested in the study of social media due to their pervasiveness in the everyday life of millions of individuals. Methodological questions and technical challenges abound as more and more data from social platforms become available for analysis. This data deluge not only yields the unprecedented opportunity to unravel questions about online individuals' behavior at scale, but also allows to explore the potential perils that the massive adoption of social media brings to our society. These communication channels provide plenty of incentives (both economical and social) and opportunities for abuse. As social media activity became increasingly intertwined with the events in the offline world, individuals and organizations have found ways to exploit these platforms to spread misinformation, to attack and smear others, or to deceive and manipulate. During crises, social media have been effectively used for emergency response, but fear-mongering actions have also triggered mass hysteria and panic. Criminal gangs and terrorist organizations like ISIS adopt social media for propaganda and recruitment. Synthetic activity and social bots have been used to coordinate orchestrated astroturf campaigns, to manipulate political elections and the stock market. The lack of effective content verification systems on many of these platforms, including Twitter and Facebook, rises concerns when younger users become exposed to cyber-bulling, harassment, or hate speech, inducing risks like depression and suicide. This article illustrates some of the recent advances facing these issues and discusses what it remains to be done, including the challenges to address in the future to make social media a more useful and accessible, safer and healthier environment for all users.
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Digital technologies have given rise to a new combination of big data and computational practices which allow for massive, latent data collection and sophisticated computational modeling, increasing the capacity of those with resources and access to use these tools to carry out highly effective, opaque and unaccountable campaigns of persuasion and social engineering in political, civic and commercial spheres. I examine six intertwined dynamics that pertain to the rise of computational politics: the rise of big data, the shift away from demographics to individualized targeting, the opacity and power of computational modeling, the use of persuasive behavioral science, digital media enabling dynamic real-time experimentation, and the growth of new power brokers who own the data or social media environments. I then examine the consequences of these new mechanisms on the public sphere and political campaigns.
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Based on a survey of participants in Egypt's Tahrir Square protests, we demonstrate that social media in general, and Facebook in particular, provided new sources of information the regime could not easily control and were crucial in shaping how citizens made individual decisions about participating in protests, the logistics of protest, and the likelihood of success. We demonstrate that people learned about the protests primarily through interpersonal communication using Facebook, phone contact, or face-to-face conversation. Controlling for other factors, social media use greatly increased the odds that a respondent attended protests on the first day. Half of those surveyed produced and disseminated visuals from the demonstrations, mainly through Facebook.
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Recent work in machine learning and natural language processing has studied the health content of tweets and demonstrated the potential for extracting useful public health information from their aggregation. This article examines the types of health topics discussed on Twitter, and how tweets can both augment existing public health capabilities and enable new ones. The author also discusses key challenges that researchers must address to deliver high-quality tools to the public health community.
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Many man made and naturally occurring phenomena, including city sizes, incomes, word frequencies, and earthquake magnitudes, are distributed according to a power-law distribution. A power-law implies that small occurrences are extremely common, whereas large instances are extremely rare. This regularity or 'law ' is sometimes also referred to as Zipf and sometimes Pareto. To add to the confusion, the laws alternately refer to ranked and unranked distributions. Here we show that all three terms, Zipf, power-law, and Pareto, can refer to the same thing, and how to easily move from the ranked to the unranked distributions and relate their exponents. A line appears on a log-log plot. One hears shouts of "Zipf!","power-law!","Pareto"! Well, which one is it? The answer is that it's quite possibly all three. Let's try to disentangle some of the confusion surrounding these matters and then tie it all back neatly together. All three terms are used to describe phenomena where large events are rare, but small ones quite common. For example, there are few large earthquakes but many small ones. There are a few mega-cities, but many small towns. There are few words, such as 'and ' and 'the ' that occur very frequently, but many which occur rarely. Zipf's law usually refers to the 'size ' y of an occurrence of an event relative to it's rank r. George Kingsley Zipf, a Harvard linguistics professor, sought to determine the 'size ' of the 3rd or 8th or 100th most common word. Size here denotes the
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Identifying social influence in networks is critical to understanding how behaviors spread. We present a method that uses in vivo randomized experimentation to identify influence and susceptibility in networks while avoiding the biases inherent in traditional estimates of social contagion. Estimation in a representative sample of 1.3 million Facebook users showed that younger users are more susceptible to influence than older users, men are more influential than women, women influence men more than they influence other women, and married individuals are the least susceptible to influence in the decision to adopt the product offered. Analysis of influence and susceptibility together with network structure revealed that influential individuals are less susceptible to influence than noninfluential individuals and that they cluster in the network while susceptible individuals do not, which suggests that influential people with influential friends may be instrumental in the spread of this product in the network.
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Sybil accounts are fake identities created to unfairly increase the power or resources of a single malicious user. Researchers have long known about the existence of Sybil accounts in online communities such as file-sharing systems, but have not been able to perform large scale measurements to detect them or measure their activities. In this paper, we describe our efforts to detect, characterize and understand Sybil account activity in the Renren online social network (OSN). We use ground truth provided by Renren Inc. to build measurement based Sybil account detectors, and deploy them on Renren to detect over 100,000 Sybil accounts. We study these Sybil accounts, as well as an additional 560,000 Sybil accounts caught by Renren, and analyze their link creation behavior. Most interestingly, we find that contrary to prior conjecture, Sybil accounts in OSNs do not form tight-knit communities. Instead, they integrate into the social graph just like normal users. Using link creation timestamps, we verify that the large majority of links between Sybil accounts are created accidentally, unbeknownst to the attacker. Overall, only a very small portion of Sybil accounts are connected to other Sybils with social links. Our study shows that existing Sybil defenses are unlikely to succeed in today's OSNs, and we must design new techniques to effectively detect and defend against Sybil attacks.
Junk News and Bots during the French Presidential Election: What Are French Voters Sharing Over Twitter in Round Two
  • C Desigaud
  • P N Howard
  • S Bradshaw
  • B Kollanyi
  • G Bolsover
Howard, P. N., Bradshaw, S., Kollanyi, B., Desigaud, C., & Bolsover, G. (2017). Junk News and Bots during the French Presidential Election: What Are French Voters Sharing Over Twitter?. COMPROP Data Memo 2017.3.
Communiqué de presse-En Marche a été victime d'une action de piratage massive et coordonnée. https://en-marche.fr/article/communique-presse-piratage Ferrara Manipulation and abuse on social media
  • En Marche
En Marche! (2017). Communiqué de presse-En Marche a été victime d'une action de piratage massive et coordonnée. https://en-marche.fr/article/communique-presse-piratage Ferrara, E. (2015). Manipulation and abuse on social media. ACM SIGWEB Newsletter, (Spring), 4.
Reverse engineering socialbot infiltration strategies in Twitter
  • C Freitas
  • F Benevenuto
  • S Ghosh
  • A Veloso
Freitas, C., Benevenuto, F., Ghosh, S., & Veloso, A. (2015). Reverse engineering socialbot infiltration strategies in Twitter. In Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. ACM.
Opening closed regimes: what was the role of social media during the Arab Spring
  • P N Howard
  • A Duffy
  • D Freelon
  • M M Hussain
  • W Mari
  • M Maziad
Howard, P. N., Duffy, A., Freelon, D., Hussain, M. M., Mari, W., & Maziad, M. (2011). Opening closed regimes: what was the role of social media during the Arab Spring?. Project on Information Technology and Political Islam Data Memo 2011.