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.

The presence of academic journals on Twitter and its relationship with dissemination (tweets) and research impact (citations)

Abstract: The purpose of this paper is to analyze the relationship between dissemination of research papers on Twitter and its influence on research impact.,Four types of journal Twitter accounts (journal, owner, publisher and no Twitter account) were defined to observe differences in the number of tweets and citations. In total, 4,176 articles from 350 journals were extracted from Plum Analytics. This altmetric provider tracks the number of tweets and citations for each paper. Student’s t-test for two-paired samples was used to detect significant differences between each group of journals. Regression analysis was performed to detect which variables may influence the getting of tweets and citations.,The results show that journals with their own Twitter account obtain more tweets (46 percent) and citations (34 percent) than journals without a Twitter account. Followers is the variable that attracts more tweets (s=0.47) and citations (s=0.28) but the effect is small and the fit is not good for tweets (R2=0.46) and insignificant for citations (R2=0.18).,This is the first study that tests the performance of research journals on Twitter according to their handles, observing how the dissemination of content in this microblogging network influences the citation of their papers.

Enhancing Music Recommender Systems with Personality Information and Emotional States: A Proposal.

Abstract: This position paper describes the initial research assumptions to improve music recommendations by including personality and emotional states. By including these psychological factors, we believe that the accuracy of the recommendation can be enhanced. We will give attention to how people use music to regulate their emotional states, and how this regulation is related to their personality. Furthermore, we will focus on how to acquire data from social media (i.e., microblogging sites such as Twitter) to predict the current emotional state of users. Finally, we will discuss how we plan to connect the correct emotionally laden music pieces to support the emotion regulation style of users.

Why Are You Sharing Others’ Tweets?: The Impact of Argument Quality and Source Credibility on Information Sharing Behavior

Abstract: Twitter, a 140-character microblogging social networking service, has garnered attention from researchers and practitioners due to its considerable potential for information diffusion. Prior studies on Twitter typically focused on how user traits or relationships in a network affect information diffusion. However, few studies have been conducted on how posted messages in the service influence this phenomenon. Thus, this paper focuses on posted messages (a.k.a. “tweets”) and how they affect individuals’ information sharing behaviors on Twitter. A model for investigating tweet sharing behavior on Twitter is proposed based on dual-process theory and on social cognitive theory. Results from a preliminary test show that individuals’ perceptions of the argument quality and source credibility of a received tweet play a major role in their information sharing behavior via the perceived level of usefulness of the information and self-efficacy in regard to the sharing of a received tweet. Additionally, the existence of external links in a tweet moderates the impact of argument quality on users’ attitudes toward received tweets.

Enhancing Classroom Learning Experience by Providing Structures to Microblogging-Based Activities

Abstract: Microblogging tools such as Twitter have been frequently adopted in educational settings to facilitate learning in recent years. Although the original purpose of microblogging tools is to connect with others in a wide network and instantly share what is happening to them with the rest of the world, educators have vigorously attempted to repurpose the utilization of the tool and integrate it into various educational settings to promote student learning.

Using Linguistic Features to Estimate Suicide Probability of Chinese Microblog Users

Abstract: If people with high risk of suicide can be identified through social media like microblog, it is possible to implement an active intervention system to save their lives. Based on this motivation, the current study administered the Suicide Probability Scale(SPS) to 1041 weibo users at Sina Weibo, which is a leading microblog service provider in China. Two NLP (Natural Language Processing) methods, the Chinese edition of Linguistic Inquiry and Word Count (LIWC) lexicon and Latent Dirichlet Allocation (LDA), are used to extract linguistic features from the Sina Weibo data. We trained predicting models by machine learning algorithm based on these two types of features, to estimate suicide probability based on linguistic features. The experiment results indicate that LDA can find topics that relate to suicide probability, and improve the performance of prediction. Our study adds value in prediction of suicidal probability of social network users with their behaviors.