Online Social Networks and Media最新文献

The rapid proliferation of misinformation and disinformation on the Internet has brought dire consequences upon societies around the world, fostering extremism, undermining social cohesion and threatening the democratic process. This impact can be attested by recent events like the COVID-19 pandemic and the 2020 US presidential election. The impact of misinformation has been so deep and wide that several authors characterize the present historic period as the “post-truth” era. Many recent efforts seek to contain the proliferation of misinformation by automating the identification of fake news through various techniques that exploit signals derived from linguistic processing of online content, analysis of message diffusion patterns, reputation lists, etc. In this paper we describe the design, implementation of, and experimentation with Check-It, a lightweight, privacy preserving browser plugin that detects fake-news. Check-It combines knowledge extracted from a variety of signals, and outperforms state-of-the-art methods on commonly-used datasets, achieving more than 90% accuracy, as well as a smooth user experience.

Instagram has been increasingly used as a source of information especially among the youth. As a result, political figures now leverage the platform to spread opinions and political agenda. We here analyze online discussions on Instagram, notably in political topics, from a network perspective. Specifically, we investigate the emergence of communities of co-commenters, that is, groups of users who often interact by commenting on the same posts and may be driving the ongoing online discussions. In particular, we are interested in salient co-interactions, i.e., interactions of co-commenters that occur more often than expected by chance and under independent behavior. Unlike casual and accidental co-interactions which normally happen in large volumes, salient co-interactions are key elements driving the online discussions and, ultimately, the information dissemination. We base our study on the analysis of 10 weeks of data centered around major elections in Brazil and Italy, following both politicians and other celebrities. We extract and characterize the communities of co-commenters in terms of topological structure, properties of the discussions carried out by community members, and how some community properties, notably community membership and topics, evolve over time. We show that communities discussing political topics tend to be more engaged in the debate by writing longer comments, using more emojis, hashtags and negative words than in other subjects. Also, communities built around political discussions tend to be more dynamic, although top commenters remain active and preserve community membership over time. Moreover, we observe a great diversity in discussed topics over time: whereas some topics attract attention only momentarily, others, centered around more fundamental political discussions, remain consistently active over time.

In recent years, the problem of unreliable content in social networks has become a major threat, with a proven real-world impact in events like elections and pandemics, undermining democracy and trust in science, respectively. Research in this domain has focused not only on the content but also on the accounts that propagate it, with the bot detection task having been thoroughly studied. However, not all bot accounts work as unreliable content spreaders (p.e. bot for news aggregation), and not all human accounts are necessarily reliable. In this study, we try to distinguish unreliable from reliable accounts, independently of how they are operated. In addition, we work towards providing a methodology capable of coping with real-world situations by introducing the content available (restricting it by volume- and time-based batches) as a parameter of the methodology. Experiments conducted on a validation set with a different number of tweets per account provide evidence that our proposed solution produces an increase of up to 20% in performance when compared with traditional (individual) models and with cross-batch models (which perform better with different batches of tweets).

With proliferation of user generated contents in social media platforms, establishing mechanisms to automatically identify toxic and abusive content becomes a prime concern for regulators, researchers, and society. Keeping the balance between freedom of speech and respecting each other dignity is a major concern of social media platform regulators. Although, automatic detection of offensive content using deep learning approaches seems to provide encouraging results, training deep learning-based models requires large amounts of high-quality labeled data, which is often missing. In this regard, we present in this paper a new deep learning-based method that fuses a Back Translation method, and a Paraphrasing technique for data augmentation. Our pipeline investigates different word-embedding-based architectures for classification of hate speech. The back translation technique relies on an encoder–decoder architecture pre-trained on a large corpus and mostly used for machine translation. In addition, paraphrasing exploits the transformer model and the mixture of experts to generate diverse paraphrases. Finally, LSTM, and CNN are compared to seek enhanced classification results. We evaluate our proposal on five publicly available datasets; namely, AskFm corpus, Formspring dataset, Warner and Waseem dataset, Olid, and Wikipedia toxic comments dataset. The performance of the proposal together with comparison to some related state-of-art results demonstrate the effectiveness and soundness of our proposal.

Studies on online hate speech have mostly focused on the automated detection of harmful messages. Little attention has been devoted so far to the development of effective strategies to fight hate speech, in particular through the creation of counter-messages. While existing manual scrutiny and intervention strategies are time-consuming and not scalable, advances in natural language processing have the potential to provide a systematic approach to hatred management. In this paper, we introduce a novel ICT platform that NGO operators can use to monitor and analyse social media data, along with a counter-narrative suggestion tool. Our platform aims at increasing the efficiency and effectiveness of operators’ activities against islamophobia. We test the platform with more than one hundred NGO operators in three countries through qualitative and quantitative evaluation. Results show that NGOs favour the platform solution with the suggestion tool, and that the time required to produce counter-narratives significantly decreases.

In the last few years, thanks to the emergence of Web 2.0, social media has made the concept of online live events possible. Users participate more and more in long-running recurring events in social media by sharing their experiences and desires. In the last few years, thanks to the emergence of Web 2.0, social media has made the concept of online live events possible. Users participate more and more in long-running recurring events in social media by sharing their experiences and desires. This work introduces long-running live events (LRLEs), as a type of activity that span physical spaces and digital ecosystems, including social media. LRLEs encompass several individuals, organizations, and brands collaborating/competing in the same event. This provides unprecedented opportunities to understand the dynamics and behavior of event-oriented participation, through collection and analysis of data of user behaviors enabled by the Web platform, where most of the digital traces are left by users. What makes this setting interesting is that the behaviors that are traced are not focused only on one individual brand or organization, and thus allows one to understand and compare the respective roles and influence in a defined setting. In this paper we provide a high-level and multi-perspective roadmap to mine, model, and study LRLEs. Among the various aspects, we develop a multi-modal approach to solve the problem of post popularity prediction that exploits potentially influential factors within LRLE. We employ two methods for implementing feature selection, together with an automated grid search for optimizing hyper-parameters in various regression methods.

Cyberaggression has been studied in various contexts and online social platforms, and modeled on different data using state-of-the-art machine and deep learning algorithms to enable automatic detection and blocking of this behavior. Users can be influenced to act aggressively or even bully others because of elevated toxicity and aggression in their own (online) social circle. In effect, this behavior can propagate from one user and neighborhood to another, and therefore, spread in the network. Interestingly, to our knowledge, no work has modeled the network dynamics of aggressive behavior. In this paper, we take a first step towards this direction by studying propagation of aggression on social media using opinion dynamics. We propose ways to model how aggression may propagate from one user to another, depending on how each user is connected to other aggressive or regular users. Through extensive simulations on Twitter data, we study how aggressive behavior could propagate in the network. We validate our models with crawled and annotated ground truth data, reaching up to 80% $AUC$, and discuss the results and implications of our work.

Online Social Networks (OSN) have become a central means of communication and interaction between people around the world. The essence of privacy has been challenged through the past two decades as technological advances enabled benefits and social visibility to active members that share content in online communities. While OSN users share personal content with friends and colleagues, they are not always fully aware of the potential unintentional exposure of their information to various people including adversaries, social bots, fake users, spammers, or data-harvesters. Preventing this information leakage is a key objective of many security models developed for OSNs including Access Control, Relationship based models, Trust based models and Information Flow control. Following previous research, we assert that a combined approach is required to overcome the shortcoming of each model. In this paper we present a new model to protect users' privacy that is composed of three main phases addressing three of its major aspects: trust, role-based access control and information flow. This model considers a user's sub-network and classifies the user's direct connections to roles. It relies on public information such as total number of friends, age of user account, and friendship duration to characterize the quality of the network connections. It also evaluates trust between a user and members of the user's network to estimates if these members are acquaintances or adversaries based on the paths of the information flow between them. Finally, it provides more precise and viable information sharing decisions and enables better privacy control in the social network. We have evaluated our model with extensive experiments using both synthetic and real users' networks to demonstrate its ability to provide a naïve user with a good means of privacy protection. We have validated separately every phase of the model and examined the decisions obtained by two different approaches. The results show a strong correlation between the decisions made by the algorithm and the users' decisions.

Emojis or ‘picture characters’ have become ubiquitous in modern-day digital communication, including social media sharing and smartphone texting. Despite this ubiquity, many questions remain about their usage, especially with respect to global variations in language and country. These questions are important, in part because they reveal how people communicate digitally on social platforms, but also because they provide a lens through which different regions and cultures can be studied. In this paper, we conduct a principled, quantitative study to understand emoji usage in terms of linguistic and country correlates. Our study involves 30 languages and countries each, and is conducted over tens of millions of tweets collected from the Twitter decahose over an entire month. Drawing on both statistical measures and information theory, our results reveal that, not only does emoji usage have strong dependencies at both the language and country level, but that some languages and countries are much more constrained in the diversity of their emoji usage. However, we also discover that the ‘popularity’ of emojis, both globally and within the context of a given language, follows a robust and invariant trend that emerges fairly quickly (over just a day’s worth of data) and cannot be explained either by a power-law or Heap’s law-like distribution.

Social media networks have revolutionized social science research. Yet, a lack of comparative empirical analysis of these networks leave social scientists with little knowledge on the role that contextual factors play in the formation of social relations. In this paper we perform a large-scale comparison of parliamentary Twitter networks in 12 countries to improve our understanding of the influence of the country’s democratic system on network behavior and elite polarization. One year of Twitter data was collected from all members of the parliament and government in these countries, which resulted in around two million tweets by almost 6000 politicians. Social network analysis of the Twitter interactions indicates that consensual democracies are characterized by more dense parliamentary relations but also higher hierarchy and fragmentation compared to majoritarian systems. Secondly, parliaments with a high effective number of parties are more cooperative, which results in higher inter-party relations. Next to that, we show differences in the followers, mentions, and retweets networks that hold across all countries and political systems. Our empirical results correspond to established theoretical insights and highlight the relevance of institutional context as well as the platform characteristics when conducting social media research. With this research we demonstrate the importance and the opportunities of social network analysis for comparative research.