Microblogging

About: Microblogging is a research topic. Over the lifetime, 4186 publications have been published within this topic receiving 137030 citations. The topic is also known as: microblog.

Filtering of Brand-Related Microblogs Using Social-Smooth Multiview Embedding

Abstract: In recent years, we have witnessed the boom of social media platforms, through which people have been generating a lot of social media data. This data touches almost every aspect of life and may have significant societal and marketing values for a variety of corporations and organizations. Thus, the development of effective techniques for gathering and analyzing social media content has attracted much research attention. As social media data tend to be heterogeneous, conversational, and fast evolving in content, a recent work reported a multifaceted approach to gather comprehensive brand-related data by crawling data using evolving keywords, key users, similar image content, and known locations. Although such approach has been found to be effective in gathering representative data, it also brings in a lot of noise. This paper aims to develop an accurate classifier to filter out noise by taking into account the multimedia content and social nature of brand-related data. In particular, we develop a microblog filtering method based on a discriminative social-aware multiview embedding. Besides the conventional content-based features, such as textual, low-level visual features, and high-level visual semantic features, that form the three key views of microblogs, we also incorporate the brand and social relations among the microblogs to learn a discriminative and social-aware embedding. With such a learned embedding, an off-the-shelf classifier, such as SVM, can then be trained and applied to microblog filtering. We verify the efficacy of our method on noise filtering in the brand data gathering task on the Brand-Social-Net dataset. Our approach is able to achieve significantly better filtering performance and improve the quality of brand data gathering.

What Are You Worrying About on Facebook and Twitter? An Empirical Investigation of Young Social Network Site Users’ Privacy Perceptions and Behaviors

Abstract: This study examines privacy protection and behavior on two different types of social network sites (SNSs), Facebook (a traditional SNS) and Twitter (a microblogging SNS). This study examines the relationships between privacy concerns and uses of SNSs as well as between privacy concerns and uses of privacy protection on SNSs. The findings indicate that young users are more concerned about the information they provide to Facebook than Twitter. Users worry more about information on those sites being accessed by parents, teachers, or other people with authority roles than by those they know less about, such as marketers, advertisers, and those in distant relations. This study discusses implications for advertising on social media and policymaking.

Identification of influential users on Twitter: A novel weighted correlated influence measure for Covid-19

Abstract: In the era of advanced mobile technology, freedom of expression over social media has become prevalent among online users This generates a huge amount of communication that eventually forms a ground for extensive research and analysis The social network analysis allows identifying the influential people in society over microblogging platforms Twitter, being an evolving social media platform, has become increasingly vital for online dialogues, trends, and content virality Applications of discovering influential users over Twitter are manifold It includes viral marketing, brand analysis, news dissemination, health awareness spreading, propagating political movement, and opinion leaders for empowering governance In our research, we have proposed a sustainable approach, namely Weighted Correlated Influence (WCI), which incorporates the relative impact of timeline-based and trend-specific features of online users Our methodology considers merging the profile activity and underlying network topology to designate online users with an influence score, which represents the combined effect To quantify the performance of our proposed method, the Twitter trend #CoronavirusPandemic is used Also, the results are validated for another social media trend The experimental outcomes depict enhanced performance of proposed WCI over existing methods that are based on precision, recall, and F1-measure for validation

Integrating Social and Auxiliary Semantics for Multifaceted Topic Modeling in Twitter

Abstract: Microblogging platforms, such as Twitter, have already played an important role in recent cultural, social and political events. Discovering latent topics from social streams is therefore important for many downstream applications, such as clustering, classification or recommendation. However, traditional topic models that rely on the bag-of-words assumption are insufficient to uncover the rich semantics and temporal aspects of topics in Twitter. In particular, microblog content is often influenced by external information sources, such as Web documents linked from Twitter posts, and often focuses on specific entities, such as people or organizations. These external sources provide useful semantics to understand microblogs and we generally refer to these semantics as auxiliary semantics. In this article, we address the mentioned issues and propose a unified framework for Multifaceted Topic Modeling from Twitter streams. We first extract social semantics from Twitter by modeling the social chatter associated with hashtags. We further extract terms and named entities from linked Web documents to serve as auxiliary semantics during topic modeling. The Multifaceted Topic Model (MfTM) is then proposed to jointly model latent semantics among the social terms from Twitter, auxiliary terms from the linked Web documents and named entities. Moreover, we capture the temporal characteristics of each topic. An efficient online inference method for MfTM is developed, which enables our model to be applied to large-scale and streaming data. Our experimental evaluation shows the effectiveness and efficiency of our model compared with state-of-the-art baselines. We evaluate each aspect of our framework and show its utility in the context of tweet clustering.

Bridging languages in social networks: How multilingual users of Twitter connect language communities?

Abstract: Multilingual users of social media and social networking sites are invisible —even unconscious— translators. Their language skills and multicultural background could help diminish the segmentation of information spheres online by connecting different language communities. Focusing on the microblogging site Twitter, this study aims at defining and classifying language bridges by looking at the languages and structure of the social networks of multilingual individuals. The social network analysis unveils four types of structures, one of which is defined as a language bridge. The next step of this work in progress aims at providing quantitative measures of this phenomenon, which has not been yet properly described or understood.