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Data extracted from social networks like Twitter are increasingly being used to build applications and services that mine and summarize public reactions to events, such as traffic monitoring platforms, identification of epidemic outbreaks,…

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…

This paper presents a practical side-channel attack that identifies the social web service account of a visitor to an attacker's website. Our attack leverages the widely adopted user-blocking mechanism, abusing its inherent property that…

Social media sites are becoming a key factor in politics. These platforms are easy to manipulate for the purpose of distorting information space to confuse and distract voters. Past works to identify disruptive patterns are mostly focused…

We propose multi-agent reinforcement learning as a new method for modeling fake news in social networks. This method allows us to model human behavior in social networks both in unaccustomed populations and in populations that have adapted…

Recently, neural networks based on multi-task learning have achieved promising performance on fake news detection, which focus on learning shared features among tasks as complementary features to serve different tasks. However, in most of…

Social networks such as Facebook (FB) and Instagram are known for tracking user online behaviour for commercial gain. To this day, there is practically no other way of achieving privacy in said platforms other than renouncing their use.…

User representations are routinely used in recommendation systems by platform developers, targeted advertisements by marketers, and by public policy researchers to gauge public opinion across demographic groups. Computer scientists consider…

Inferring trust relations between social media users is critical for a number of applications wherein users seek credible information. The fact that available trust relations are scarce and skewed makes trust prediction a challenging task.…

Influencer Marketing generated $16 billion in 2022. Usually, the more popular influencers are paid more for their collaborations. Thus, many services were created to boost profiles' popularity metrics through bots or fake accounts. However,…

Two approaches for graph based semi-supervised learning are proposed. The firstapproach is based on iteration of an affine map. A key element of the affine map iteration is sparsematrix-vector multiplication, which has several very…

Metadata are associated to most of the information we produce in our daily interactions and communication in the digital world. Yet, surprisingly, metadata are often still catergorized as non-sensitive. Indeed, in the past, researchers and…

In the global move toward urbanization, making sure the people remaining in rural areas are not left behind in terms of development and policy considerations is a priority for governments worldwide. However, it is increasingly challenging…

User identification has been a major field of research in privacy and security topics. Users might utilize multiple Online Social Networks (OSNs) to access a variety of text, videos, and links, and connect to their friends. Identifying user…

Understanding political polarization on social platforms is important as public opinions may become increasingly extreme when they are circulated in homogeneous communities, thus potentially causing damage in the real world. Automatically…

In social network analysis, automatic social circle detection in ego-networks is becoming a fundamental and important task, with many potential applications such as user privacy protection or interest group recommendation. So far, most…

Fake news detection in social media has become increasingly important due to the rapid proliferation of personal media channels and the consequential dissemination of misleading information. Existing methods, which primarily rely on…

We study the extent to which we can infer users' geographical locations from social media. Location inference from social media can benefit many applications, such as disaster management, targeted advertising, and news content tailoring.…

When evaluating the cause of one's popularity on Twitter, one thing is considered to be the main driver: Many tweets. There is debate about the kind of tweet one should publish, but little beyond tweets. Of particular interest is the…

Large-scale digital platforms generate billions of timestamped user-item interactions (events) that are crucial for predicting user attributes in, e.g., fraud prevention and recommendations. While self-supervised learning (SSL) effectively…