相关论文: Learning from the News: Predicting Entity Populari…

Twitter has been proven to be a notable source for predictive modelling on various domains such as the stock market, the dissemination of diseases or sports outcomes. However, such a study has not been conducted in football (soccer) so far.…

Social media users post content on various topics. A defining feature of social media is that other users can provide feedback -- called community feedback -- to their content in the form of comments, replies, and retweets. We hypothesize…

Image of an entity can be defined as a structured and dynamic representation which can be extracted from the opinions of a group of users or population. Automatic extraction of such an image has certain importance in political science and…

We set up a simple mathematical model for the dynamics of public interest in terms of media coverage and social interactions. We test the model on a series of events related to violence in the US during 2020, using the volume of tweets and…

Sentiment-based stock prediction systems aim to explore sentiment or event signals from online corpora and attempt to relate the signals to stock price variations. Both the feature-based and neural-networks-based approaches have delivered…

Text generator systems have become extremely popular with the advent of recent deep learning models such as encoder-decoder. Controlling the information and style of the generated output without supervision is an important and challenging…

We tackle the problem of classifying news articles pertaining to disinformation vs mainstream news by solely inspecting their diffusion mechanisms on Twitter. Our technique is inherently simple compared to existing text-based approaches, as…

Social susceptibility is defined and analyzed using data from CNN news website. The current models of opinion dynamics, voting, and herding in closed communities are extended, and the community's response to the injection of a group with…

Twitter is used for a variety of reasons, including information dissemination, marketing, political organizing and to spread propaganda, spamming, promotion, conversations, and so on. Characterizing these activities and categorizing…

Social media users share their ideas, thoughts, and emotions with other users. However, it is not clear how online users would respond to new research outcomes. This study aims to predict the nature of the emotions expressed by Twitter…

Public opinion influences events, especially related to stock market movement, in which a subtle hint can influence the local outcome of the market. In this paper, we present a dataset that allows for company-level analysis of tweet based…

In information-rich environments, the competition for users' attention leads to a flood of content from which people often find hard to sort out the most relevant and useful pieces. Using Twitter as a case study, we applied an attention…

On daily basis, millions of Twitter accounts post a vast number of tweets including numerous Twitter entities (mentions, replies, hashtags, photos, URLs). Many of these entities are used in common by many accounts. The more common entities…

User engagement refers to the amount of interaction an instance (e.g., tweet, news, and forum post) achieves. Ranking the items in social media websites based on the amount of user participation in them, can be used in different…

What tweet features are associated with higher effectiveness in tweets? Through the mining of 122 million engagements of 2.5 million original tweets, we present a systematic review of tweet time, entities, composition, and user account…

Popularity of content in social media is unequally distributed, with some items receiving a disproportionate share of attention from users. Predicting which newly-submitted items will become popular is critically important for both…

In this paper, we present computational models to predict Twitter users' attitude towards a specific brand through their personal and social characteristics. We also predict their likelihood to take different actions based on their…

Measuring the impact and success of human performance is common in various disciplines, including art, science, and sports. Quantifying impact also plays a key role on social media, where impact is usually defined as the reach of a user's…

Being able to recommend links between users in online social networks is important for users to connect with like-minded individuals as well as for the platforms themselves and third parties leveraging social media information to grow their…

Social media such as tweets are emerging as platforms contributing to situational awareness during disasters. Information shared on Twitter by both affected population (e.g., requesting assistance, warning) and those outside the impact zone…