Popular Internet services are fundamentally shaping and reshaping traditional ways of people communication, thus having a major impact on their social life. Two of the very popular Internet services with this characteristic are Online Social Networks (OSNs) and Peer-to-Peer (P2P) systems. OSNs provide a virtual environment where people can share their information and interests as well as being in contact with other people. On the other hand, P2P systems, which are still one of the popular services with a large proportion of the whole Internet traffic, provide a golden opportunity for their customers to share different type of content including copyrighted content. Apart from the huge popularity of OSNs and P2P systems among regular users, they are being intensively used by professional players (big companies, politician, athletes, celebrities in case of OSNs and professional content publishers in case of P2P) in order to interact with people for different purposes (marketing campaigns, customer feedback, public reputation improvement, etc.). In this thesis, we characterize the behavior of regular and professional users in the two mentioned popular services (OSNs and P2P systems) in terms of publishing strategies, content consumption and behavioral analysis. To this end, five of our conducted studies are presented in this manuscript as follows: - "The evolution of multimedia contents", which presents a thorough analysis on the evolution of multimedia content available in BitTorrent by focusing on four relevant metrics across different content categories: content availability, content popularity, content size and user's feedback. - "The reaction of professional users to antipiracy actions", by examining the impact of two major antipiracy actions, the closure of Megaupload and the implementation of the French antipiracy law (HADOPI), on professional publishers behavior in the largest BitTorrent portal who are major providers of online copyrighted content. - "The amount of disclosed information on Facebook", by ...
Popular Internet services are fundamentally shaping and reshaping traditional ways of people communication, thus having a major impact on their social life. Two of the very popular Internet services with this characteristic are Online Social Networks (OSNs) and Peer-to-Peer (P2P) systems. OSNs provide a virtual environment where people can share their information and interests as well as being in contact with other people. On the other hand, P2P systems, which are still one of the popular services with a large proportion of the whole Internet traffic, provide a golden opportunity for their customers to share different type of content including copyrighted content. Apart from the huge popularity of OSNs and P2P systems among regular users, they are being intensively used by professional players (big companies, politician, athletes, celebrities in case of OSNs and professional content publishers in case of P2P) in order to interact with people for different purposes (marketing campaigns, customer feedback, public reputation improvement, etc.). In this thesis, we characterize the behavior of regular and professional users in the two mentioned popular services (OSNs and P2P systems) in terms of publishing strategies, content consumption and behavioral analysis. To this end, five of our conducted studies are presented in this manuscript as follows: - "The evolution of multimedia contents", which presents a thorough analysis on the evolution of multimedia content available in BitTorrent by focusing on four relevant metrics across different content categories: content availability, content popularity, content size and user's feedback. - "The reaction of professional users to antipiracy actions", by examining the impact of two major antipiracy actions, the closure of Megaupload and the implementation of the French antipiracy law (HADOPI), on professional publishers behavior in the largest BitTorrent portal who are major providers of online copyrighted content. - "The amount of disclosed information on Facebook", by investigating the public exposure of Facebook users' profile attributes in a large dataset including half million regular users. - "Professional users Cross Posting Activity", by analyzing the publishing pattern of professional users which includes same information over three major OSNs namely Facebook, Google+ and Twitter. - "Professional Users' Strategies in OSNs", where we investigate the global strategy of professional users by sector (e.g., Cars companies, Clothing companies, Politician, etc.) over Facebook, Google+ and Twitter. The outcomes of this thesis provide an overall vision to understand some important behavioral aspects of different types of users on popular Internet services and these contributions can be used in various domains (e.g. marketing analysis and advertising campaign, etc.) and different parties can benefit from the results and the implemented methodologies such as ISPs and owners of the Services for their future planning or expansion of the current services as well as professional players to increase their success on social media ; Les services Internet populaires modèlent et remodèlent fondamentalement les moyens traditionnels de communication des personnes, ayant ainsi un impact majeur sur leur vie sociale. Deux des services Internet très populaires avec cette caractéristique sont les Réseaux sociaux en ligne (OSN) et les systèmes Peer-to-Peer (P2P). Les ONS fournissent un environnement virtuel où les gens peuvent partager leurs informations et leurs intérêts tout en étant en contact avec d'autres personnes. D'autre part, les systèmes P2P, qui sont toujours l'un des services populaires avec une grande proportion de l'ensemble du trafic Internet, offrent une occasion en or pour leurs clients de partager un type de contenu différent, y compris le contenu protégé. En dehors de l'énorme popularité des ONS et des systèmes de P2P parmi les utilisateurs réguliers, ils sont intensivement utilisés par les professionnels (grandes entreprises, politiciens, athlètes, célébrités en cas d'ONS et éditeurs de contenu professionnels en cas de P2P) afin d'interagir avec les gens à des fins différentes (campagnes marketing, les commentaires des clients, amélioration de la réputation publique, etc.) Dans cette thèse, nous caractérisons le comportement des utilisateurs réguliers et professionnels dans les deux services mentionnés populaires (ONS et P2P) en termes de stratégies de publication, de consommation de contenu et d'analyse comportementale. À cette fin, cinq de nos études menées sont présentées dans ce manuscrit comme suit: - "L'évolution des contenus multimédias", qui présente une analyse approfondie sur l'évolution du contenu multimédia disponible en BitTorrent en se concentrant sur quatre mesures pertinentes à travers différentes catégories de contenu : la disponibilité du contenu, la popularité du contenu, la taille de contenu et les commentaires de l'utilisateur - "La réaction des utilisateurs professionnels face aux actions de lutte contre le piratage", en examinant l'impact de deux grandes actions de lutte contre le piratage - la fermeture de Megaupload et la mise en œuvre de la loi anti-piratage française (HADOPI) - sur le comportement des publicateurs professionnels dans le plus grand portail de BitTorrent qui sont les principaux fournisseurs de contenu en ligne protégé. - "La quantité d'informations divulguées sur Facebook", en enquêtant sur l'exposition publique des profils utilisateurs, une grande base de données comprenant un demi-million d'utilisateurs réguliers. - "Les utilisateurs professionnels Cross Posting Activity», en analysant le modèle de publication des utilisateurs professionnels de mêmes informations sur trois grands ONS à savoir Facebook, Google+ et Twitter. - "Les stratégies des utilisateurs professionnels dans les ONS", où nous étudions la stratégie globale d'utilisateurs professionnels par secteur (par exemple, les entreprises de voitures, l'habillement, politiques, etc.) sur Facebook, Google+ et Twitter. Les résultats de cette thèse fournissent une vision d'ensemble pour comprendre certains aspects comportementaux importants de différents types d'utilisateurs des services Internet populaires et ces contributions peuvent être utilisées dans divers domaines (par exemple analyse de campagne marketing et publicité, etc.) et les différentes parties peuvent bénéficier des résultats et des méthodologies mises en œuvre telles que les FAI et les propriétaires des services pour leur planification ou l'expansion des services actuels à venir, ainsi que les professionnels pour accroître leur succès sur les médias sociaux
International audience ; Impersonators on Online Social Networks such as Instagram are playing an important role in the propagation of the content. These entities are the type of nefarious fake accounts that intend to disguise a legitimate account by making similar profiles. In addition to having impersonated profiles, we observed a considerable engagement from these entities to the published posts of verified accounts. Toward that end, we concentrate on the engagement of impersonators in terms of active and passive engagements which is studied in three major communities including "Politician", "News agency", and "Sports star" on Instagram. Inside each community, four verified accounts have been selected. Based on implemented approach in our previous studies [1], we have collected 4.8K comments, and 2.6K likes across 566 posts created from 3.8K impersonators during 7 months. Our study shed light into this interesting phenomena and provides a surprising observation that can help us to understand better how impersonators engaging themselves inside Instagram in terms of writing Comments and leaving Likes.
International audience ; Impersonators on Online Social Networks such as Instagram are playing an important role in the propagation of the content. These entities are the type of nefarious fake accounts that intend to disguise a legitimate account by making similar profiles. In addition to having impersonated profiles, we observed a considerable engagement from these entities to the published posts of verified accounts. Toward that end, we concentrate on the engagement of impersonators in terms of active and passive engagements which is studied in three major communities including "Politician", "News agency", and "Sports star" on Instagram. Inside each community, four verified accounts have been selected. Based on implemented approach in our previous studies [1], we have collected 4.8K comments, and 2.6K likes across 566 posts created from 3.8K impersonators during 7 months. Our study shed light into this interesting phenomena and provides a surprising observation that can help us to understand better how impersonators engaging themselves inside Instagram in terms of writing Comments and leaving Likes.
International audience ; There is an ever-growing number of users who duplicate the social media accounts of celebrities or generally impersonate their presence on online social media as well as Instagram. Of course, this has led to an increasing interest in detecting fake profiles and investigating their behaviour. We begin this research by targeting a few famous politicians, including Donald J. Trump, Barack Obama, and Emmanuel Macron and collecting their activity for the period of 3 months using a specifically-designed crawler across Instagram. We then experimented with several profile characteristics such as user-name, display name, biography, and profile picture to identify impersonator among 1,5M unique users. Using publicly crawled data, our model was able to distinguish crowds of impersonators and political bots. We continued by providing an analysis of the characteristics and behaviour of these impersonators. Finally, we conclude the analysis by classifying impersonators into four different categories.
International audience ; There is an ever-growing number of users who duplicate the social media accounts of celebrities or generally impersonate their presence on online social media as well as Instagram. Of course, this has led to an increasing interest in detecting fake profiles and investigating their behaviour. We begin this research by targeting a few famous politicians, including Donald J. Trump, Barack Obama, and Emmanuel Macron and collecting their activity for the period of 3 months using a specifically-designed crawler across Instagram. We then experimented with several profile characteristics such as user-name, display name, biography, and profile picture to identify impersonator among 1,5M unique users. Using publicly crawled data, our model was able to distinguish crowds of impersonators and political bots. We continued by providing an analysis of the characteristics and behaviour of these impersonators. Finally, we conclude the analysis by classifying impersonators into four different categories.
International audience ; Fake accounts and Impersonators on Online Social Networks such as Instagram are turning difficulties for society. This has attended to an increasing interest in detecting fake profiles and investigating their behaviours. Questions like who are impersonators? what are their characteristics? and are they bots? will arise. To answer, we begin this research by collecting data from three important communities on Instagram including "Politician", "News agency", and "Sports star". Inside each community, four verified top accounts are picked. Based on the users who reacted to their published posts, we detect 4K impersonators [1]. Then we employed well-known clustering methods to distribute impersonators into separated clusters to observe obscure behaviours and unusual profile characteristics. We also studied the cross-group analysis of clusters inside each community to explore engagements. Finally, we conclude the study by providing a complete investigation of the bot-like cluster
International audience ; There is an ever-growing number of users who duplicate the social media accounts of celebrities or generally impersonate their presence on online social media as well as Instagram. Of course, this has led to an increasing interest in detecting fake profiles and investigating their behaviour. We begin this research by targeting a few famous politicians, including Donald J. Trump, Barack Obama, and Emmanuel Macron and collecting their activity for the period of 3 months using a specifically-designed crawler across Instagram. We then experimented with several profile characteristics such as user-name, display name, biography, and profile picture to identify impersonator among 1,5M unique users. Using publicly crawled data, our model was able to distinguish crowds of impersonators and political bots. We continued by providing an analysis of the characteristics and behaviour of these impersonators. Finally, we conclude the analysis by classifying impersonators into four different categories.
International audience ; Fake accounts and Impersonators on Online Social Networks such as Instagram are turning difficulties for society. This has attended to an increasing interest in detecting fake profiles and investigating their behaviours. Questions like who are impersonators? what are their characteristics? and are they bots? will arise. To answer, we begin this research by collecting data from three important communities on Instagram including "Politician", "News agency", and "Sports star". Inside each community, four verified top accounts are picked. Based on the users who reacted to their published posts, we detect 4K impersonators [1]. Then we employed well-known clustering methods to distribute impersonators into separated clusters to observe obscure behaviours and unusual profile characteristics. We also studied the cross-group analysis of clusters inside each community to explore engagements. Finally, we conclude the study by providing a complete investigation of the bot-like cluster
International audience ; Social networking sites (SNSs) facilitate the sharing of ideas and information through different types of feedback including publishing posts, leaving comments and other type of reactions. However, some comments or feedback on SNSs are inconsiderate and offensive, and sometimes this type of feedback has a very negative effect on a target user. The phenomenon known as flaming goes hand-in-hand with this type of posting that can trigger almost instantly on SNSs. Most popular users such as celebrities, politicians and news media are the major victims of the flaming behaviors and so detecting these types of events will be useful and appreciated. Flaming event can be monitored and identified by analyzing negative comments received on a post. Thus, our main objective of this study is to identify a way to detect flaming events in SNS using a sentiment prediction method. We use a deep Neural Network (NN) model that can identity sentiments of variable length sentences and classifies the sentiment of SNSs content (both comments and posts) to discover flaming events. Our deep NN model uses Word2Vec and FastText word embedding methods as its training to explore which method is the most appropriate. The labeled dataset for training the deep NN is generated using an enhanced lexicon based approach. Our deep NN model classifies the sentiment of a sentence into five classes: Very Positive, Positive, Neutral, Negative and Very Negative. To detect flaming incidents, we focus only on the comments classified into the Negative and Very Negative classes. As a use-case, we try to explore the flaming phenomena in the news media domain and therefore we focused on news items posted by three popular news media on Facebook (BBCNews, CNN and FoxNews) to train and test the model. The experimental results show that flaming events can be detected with our proposed approach, and we explored main characteristics that trigger a flaming event and topics discussed in the flaming posts.
International audience ; Social networking sites (SNSs) facilitate the sharing of ideas and information through different types of feedback including publishing posts, leaving comments and other type of reactions. However, some comments or feedback on SNSs are inconsiderate and offensive, and sometimes this type of feedback has a very negative effect on a target user. The phenomenon known as flaming goes hand-in-hand with this type of posting that can trigger almost instantly on SNSs. Most popular users such as celebrities, politicians and news media are the major victims of the flaming behaviors and so detecting these types of events will be useful and appreciated. Flaming event can be monitored and identified by analyzing negative comments received on a post. Thus, our main objective of this study is to identify a way to detect flaming events in SNS using a sentiment prediction method. We use a deep Neural Network (NN) model that can identity sentiments of variable length sentences and classifies the sentiment of SNSs content (both comments and posts) to discover flaming events. Our deep NN model uses Word2Vec and FastText word embedding methods as its training to explore which method is the most appropriate. The labeled dataset for training the deep NN is generated using an enhanced lexicon based approach. Our deep NN model classifies the sentiment of a sentence into five classes: Very Positive, Positive, Neutral, Negative and Very Negative. To detect flaming incidents, we focus only on the comments classified into the Negative and Very Negative classes. As a use-case, we try to explore the flaming phenomena in the news media domain and therefore we focused on news items posted by three popular news media on Facebook (BBCNews, CNN and FoxNews) to train and test the model. The experimental results show that flaming events can be detected with our proposed approach, and we explored main characteristics that trigger a flaming event and topics discussed in the flaming posts.
International audience ; Social networking sites (SNSs) facilitate the sharing of ideas and information through different types of feedback including publishing posts, leaving comments and other type of reactions. However, some comments or feedback on SNSs are inconsiderate and offensive, and sometimes this type of feedback has a very negative effect on a target user. The phenomenon known as flaming goes hand-in-hand with this type of posting that can trigger almost instantly on SNSs. Most popular users such as celebrities, politicians and news media are the major victims of the flaming behaviors and so detecting these types of events will be useful and appreciated. Flaming event can be monitored and identified by analyzing negative comments received on a post. Thus, our main objective of this study is to identify a way to detect flaming events in SNS using a sentiment prediction method. We use a deep Neural Network (NN) model that can identity sentiments of variable length sentences and classifies the sentiment of SNSs content (both comments and posts) to discover flaming events. Our deep NN model uses Word2Vec and FastText word embedding methods as its training to explore which method is the most appropriate. The labeled dataset for training the deep NN is generated using an enhanced lexicon based approach. Our deep NN model classifies the sentiment of a sentence into five classes: Very Positive, Positive, Neutral, Negative and Very Negative. To detect flaming incidents, we focus only on the comments classified into the Negative and Very Negative classes. As a use-case, we try to explore the flaming phenomena in the news media domain and therefore we focused on news items posted by three popular news media on Facebook (BBCNews, CNN and FoxNews) to train and test the model. The experimental results show that flaming events can be detected with our proposed approach, and we explored main characteristics that trigger a flaming event and topics discussed in the flaming posts.
Today's Internet traffic is mostly dominated by multimedia content and the prediction is that this trend will intensify in the future. Therefore, main Internet players, such as ISPs, content delivery platforms (e. g. Youtube, Bitorrent, Netflix, etc) or CDN operators, need to understand the evolution of multimedia content availability and popularity in order to adapt their infrastructures and resources to satisfy clients requirements while they minimize their costs. This paper presents a thorough analysis on the evolution of multimedia content available in BitTorrent. Specifically, we analyze the evolution of four relevant metrics across different content categories: content availability, content popularity, content size and user's feedback. To this end we leverage a large-scale dataset formed by four snapshots collected from the most popular BitTorrent portal, namely The Pirate Bay, between Nov. 2009 and Feb. 2012. Overall our dataset is formed by more than 160k content that attracted more than 185M of download sessions. ; The research leading to these results was funded by the European Union under the project eCOUSIN (EU-FP7-318398) and the project TWIRL (ITEA2-Call 5-10029), the Spanish MECD under the CRAMNET project (TEC2012-38362-C03- 01), the Spanish Ministry of Economy and Competitiveness under the eeCONTENT project (TEC2011-29688-C02-02), and the General Directorate of Universities and Research of the Regional Government of Madrid under the MEDIANET Project (S2009/TIC-1468). ; Publicado
During recent years, a few countries have put in place online antipiracy laws and there has been some major enforcement actions against violators. This raises the question that to what extent antipiracy actions have been effective in deterring online piracy? This is a challenging issue to explore because of the difficulty to capture user behavior, and to identify the subtle effect of various underlying (and potentially opposing) causes. In this paper, we tackle this question by examining the impact of two major antipiracy actions, the closure of Megaupload and the implementation of the French antipiracy law, on publishers in the largest BitTorrent portal who are major providers of copyrighted content online. We capture snapshots of BitTorrent publishers at proper times relative to the targeted antipiracy event and use the trends in the number and the level of activity of these publishers to assess their reaction to these events. Our investigation illustrates the importance of examining the impact of antipiracy events on different groups of publishers and provides valuable insights on the effect of selected major antipiracy actions on publishers' behavior. ; This work has been partially supported by the European Union through the FP7 eCOUSIN (318398) and TREND (257740) Projects and the ITEA2 TWIRL Project (Call 5-10029), the Spanish Government under the CRAMNET project (TEC2012-38362-C03-01) and eeCONTENT Project (TEC2011- 29688-C02-02), the Regional Government of Madrid through the MEDIANET project (S-2009/TIC-1468), and the National Science Foundation under Grant IIS-0917381. ; European Community's Seventh Framework Program
