Related papers: Twitter User Representation Using Weakly Supervise…

User representation learning is vital to capture diverse user preferences, while it is also challenging as user intents are latent and scattered among complex and different modalities of user-generated data, thus, not directly measurable.…

Social network and publishing platforms, such as Twitter, support the concept of verification. Verified accounts are deemed worthy of platform-wide public interest and are separately authenticated by the platform itself. There have been…

Social world knowledge is a key ingredient in effective communication and information processing by humans and machines alike. As of today, there exist many knowledge bases that represent factual world knowledge. Yet, there is no resource…

Twitter bot detection has become an important and challenging task to combat misinformation and protect the integrity of the online discourse. State-of-the-art approaches generally leverage the topological structure of the Twittersphere,…

Social Networks represent one of the most important online sources to share content across a world-scale audience. In this context, predicting whether a post will have any impact in terms of engagement is of crucial importance to drive the…

Social divide and polarization have become significant societal issues. To understand the mechanisms behind these phenomena, social media analysis offers research opportunities in computational social science, where developing effective…

With the increasing abundance of 'digital footprints' left by human interactions in online environments, e.g., social media and app use, the ability to model complex human behavior has become increasingly possible. Many approaches have been…

Information about individuals can help to better understand what they say, particularly in social media where texts are short. Current approaches to modelling social media users pay attention to their social connections, but exploit this…

Twitter is one of the most prominent Online Social Networks. It covers a significant part of the online worldwide population~20% and has impressive growth rates. The social graph of Twitter has been the subject of numerous studies since it…

We present a highly effective unsupervised framework for detecting the stance of prolific Twitter users with respect to controversial topics. In particular, we use dimensionality reduction to project users onto a low-dimensional space,…

Social media contains useful information about people and the society that could help advance research in many different areas (e.g. by applying opinion mining, emotion/sentiment analysis, and statistical analysis) such as business and…

With 93% of pro-marijuana population in US favoring legalization of medical marijuana, high expectations of a greater return for Marijuana stocks, and public actively sharing information about medical, recreational and business aspects…

Inferring latent attributes of people online is an important social computing task, but requires integrating the many heterogeneous sources of information available on the web. We propose learning individual representations of people using…

Nowadays, social media plays an important role in many fields, such as the promotion of measures against major infectious diseases, merchandising, etc. In social media, some people are known as opinion leaders due to their strong ability to…

The evolution of social media users' behavior over time complicates user-level comparison tasks such as verification, classification, clustering, and ranking. As a result, na\"ive approaches may fail to generalize to new users or even to…

Social media user representation learning aims to capture user preferences, interests, and behaviors in low-dimensional vector representations. These representations are critical to a range of social problems, including predicting user…

Research in analysis of microblogging platforms is experiencing a renewed surge with a large number of works applying representation learning models for applications like sentiment analysis, semantic textual similarity computation, hashtag…

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…

In this study, we propose a novel graph-based approach to model, analyze and comprehend user interactions within a social media platform based on post-comment relationship. We construct a user interaction graph from social media data and…

We demonstrate that user preferences can be represented and predicted across topical domains using large-scale social modeling. Given information about popular entities favored by a user, we project the user into a social embedding space…