Quick Detection of High-degree Entities in Large Directed Networks

In this paper, we address the problem of quick detection of high-degree entities in large online social networks. Practical importance of this problem is attested by a large number of companies that continuously collect and update statistics about popular entities, usually using the degree of an entity as an approximation of its popularity. We suggest a simple, efficient, and easy to implement two-stage randomized algorithm that provides highly accurate solutions for this problem. For instance, our algorithm needs only one thousand API requests in order to find the top-100 most followed users in Twitter, a network with approximately a billion of registered users, with more than 90% precision. Our algorithm significantly outperforms existing methods and serves many different purposes, such as finding the most popular users or the most popular interest groups in social networks. An important contribution of this work is the analysis of the proposed algorithm using Extreme Value Theory -- a branch of probability that studies extreme events and properties of largest order statistics in random samples. Using this theory, we derive an accurate prediction for the algorithm's performance and show that the number of API requests for finding the top-k most popular entities is sublinear in the number of entities. Moreover, we formally show that the high variability among the entities, expressed through heavy-tailed distributions, is the reason for the algorithm's efficiency. We quantify this phenomenon in a rigorous mathematical way

Algorithmes et techniques de détection des bots dans les réseaux sociaux

Dans cette thèse nous proposons des techniques d'apprentissage automatique ayant comme but la détection et caractérisation des bots malveillants dans les réseaux sociaux. Une nouveauté de ces méthodes est qu'uniquement des motifs d'interaction avec des " amis " des comptes analysés sont utilisés comme source de données pour la détection des bots. Les techniques proposées ont plusieurs nouveaux avantages. Il n'y a plus de nécessité de télécharger des gros volumes de données textuelles et médiatiques, qui dépendent fortement du langage. Cela permet aussi détecter des bots cachés par des paramètres de confidentialité ou bloqués, des bots camouflés imitant des personnes réelles, les groupes de bots, et estimer la qualité et le prix d'un bot. Dans une solution que nous avons développée, nous proposons extraire des données pour l'analyse sous la forme des graphes sociaux, utilisant un modèle de réseau social hiérarchisé. Après, afin de déterminer des paramètres, nous utilisons les méthodes statistiques, algorithmes de graphes, et les méthodes nous permettant d'analyser le plongement de graphe. La décision finale est prise utilisant le modèle de foret aléatoire ou le réseau de neurones. A la base de ce schéma, nous proposons 4 techniques nous permettant de réaliser le cycle complet de détection des attaques - 2 techniques de détection des bots (détection individuelle et détection de groupe); et 2 techniques pour les caractériser - l'estimation de qualité et l'estimation de prix. La thèse aussi présente des expériences permettant à évaluer les solutions proposées. Comme exemple le réseau social VKontacte a été choisi. A ce but, nous avons développé le logiciel prototype qui peut effectuer toute la chaine d'analyse, de collection des données à la prise de décision. Et afin d'entrainer nos modèles, nous avons obtenu directement de vendeurs les données concernant les bots de qualité, prix et stratégies de camouflage différentes. L'étude a montré qu'en utilisant uniquement l'information concernant les graphes des amis il est possible de reconnaitre et caractériser les bots très efficacement (AUC-ROC ~ 0.9). En même temps, la solution proposée est robuste par rapport à l'émergence de nouveaux types des bots, et au changement de leur type - de bots générés automatiquement et comptes piratés jusqu'aux utilisateurs humaines qui se chargent de l'activité malveillante contre une rémunération.In this thesis, we propose machine learning techniques to detecting and characterizing malicious bots in social networks. The novelty of these techniques is that only interaction patterns of friends' of analysed accounts are used as the source data to detect bots. The proposed techniques have a number of novel advantages. There is no need to download a large amount of text and media data, which are highly language-dependent. Furthermore, it allows one to detect bots that are hidden by privacy settings or blocked, to detect cam- ouflages bots that mimic real people, to detect a group of bots, and to estimate quality and price of a bot. In the developed solution, we propose to extract the input data for the analysis in form of a social graphs, using a hierarchical social network model. After, to construct features from this graph we use statistical methods, graph algorithms, and methods that analyze graph embedding. And finally, we make a decision using a random forest model or a neural network. Based on this schema we propose 4 techniques, that allows one to perform the full cycle attack detection pipeline - 2 techniques for bot detection: individual bot detection, and group bot detection; and 2 techniques for characterization of bots: estimation of bot quality, and estimation of bot price. The thesis also presents experiments that evaluate the proposed solutions on the example of bot detection in VKontakte social network. For this, we developed the software prototype that implements the entire chain of analysis - from data collection to decision making. And in order to train the models, we collected the data about bots with different quality, price and camouflage strategies directly from the bot sellers. The study showed that using only information about the graphs of friends it is possible to recognize and characterize bots with high efficiency (AUC - ROC ˜ 0.9). At the same time, the proposed solution is quite resistant to the emergence of new types of bots, and to bots of various types - from automatically generated and hacked accounts to users that perform malicious activity for money

Social Bots As an Instrument of Influence in Social Networks: Typologization Problems

Nowadays, in the field of social bots investigations, we can observe a new research trend — a shift from a technology-centered to sociology-centered interpretations. It leads to the creation of new perspectives for sociology: now the phenomenon of social bots is not only considered as one of the efficient manipulative technologies but has a wider meaning: new communicative technologies have an informational impact on the social networks space. The objective of this research is to assess the new approaches of the established social bots typologies (based on the fields of their usage, objectives, degree of human behavior imitation), and also consider the ambiguity and controversy of the use of such typologies using the example of botnets operating in the VKontakte social network. A method of botnet identification is based on comprehensive methodology developed by the authors which includes the frequency analysis of published messages, botnet profiling, statistical analysis of content, analysis of botnet structural organization, division of content into semantic units, forming content clusters, content analysis inside the clusters, identification of extremes — maximum number of unique texts published by botnets in a particular cluster for a certain period. The methodology was applied for the botnet space investigation of Russian online social network VKontakte in February and October 2018. The survey has fixed that among 10 of the most active performing botnets, three botnets were identified that demonstrate the ambiguity and controversy of their typologization according to the following criteria: botnet “Defrauded shareholders of LenSpetsStroy” — according to the field of their usage, botnet “Political news in Russian and Ukrainian languages” — according to their objectives, botnet “Ksenia Sobchak” — according to the level of human behavior imitation. The authors identified the prospects for sociological analysis of different types of bots in a situation of growing accessibility and routinization of bot technologies used in social networks. Keywords: social bots, botnets, classification, VKontakte social networ

Political polarisation on social media in different national contexts

The present dissertation examines the phenomenon of political polarisation on social media. Specifically, the dissertation addresses the question of how the intensity of polarisation and the ideological lines along which it occurs might vary between different national contexts. First, it explores the differences in the intensity of political polarisation on Twitter in 16 democratic countries (Article 1). Second, it examines the ideological lines along which polarisation occurs in two non-Western contexts, specifically among Russian (Article 2) and Ukrainian (Article 3) users of Vkontakte – a social media platform popular among users from post-Soviet states. The dissertation demonstrates that the levels of political polarisation differ dramatically between countries. In democracies, polarisation tends to be lowest in multi-party systems with proportional electoral rules (e.g., Sweden), and the highest in pluralist two-party systems (e.g., United States). It also shows that, in non-democratic non- Western contexts, polarisation does not necessarily run along the left–right spectrum or party system lines. In authoritarian regimes or those with less stable party systems, polarisation runs along the lines of other issues that are more politically relevant in a given context. In Russia, polarisation manifests itself along pro-regime vs anti-regimes lines, whereas in Ukraine, polarisation happens around geopolitical issues. Polarisation on social media thus tends to reflect existing political cleavages and their intensity, in line with the theory of political parallelism. The major implication of this dissertation in the context of research into polarisation on social media is that findings on the topic from single-country studies that come from Western democratic contexts should be interpreted with caution, as they are not necessarily generalisable. To make generalisable inferences about the relationship between social media and political polarisation, more comparative studies are needed, as well as studies that take into account platform affordances and the causal mechanisms that might drive polarisation

There can be only one truth: Ideological segregation and online news communities in Ukraine

The paper examines ideological segregation among Ukrainian users in online environments, using as a case study partisan news communities on Vkontakte, the largest online platform in post-communist states. Its findings suggest that despite their insignificant numbers, partisan news communities attract substantial attention from Ukrainian users and can encourage the formation of isolated ideological cliques – or ‘echo chambers’ – that increase societal polarisation. The paper also investigates factors that predict users’ interest in partisan content and establishes that the region of residence is the key predictor of selective consumption of pro-Ukrainian or pro-Russian partisan news content