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.

A Study on Detecting Patterns in Twitter Intra-topic User and Message Clustering

Abstract: Timely detection of hidden patterns is the key for the analysis and estimating of driving determinants for mission critical decision making. This study applies Cheong and Lee’s “context-aware” content analysis framework to extract latent properties from Twitter messages (tweets). In addition, we incorporate an unsupervised Self-organizing Feature Map (SOM) as a machine learning-based clustering tool that has not been investigated in the context of opinion mining and sentimental analysis using microblogging. Our experimental results reveal the detection of interesting patterns for topics of interest which are latent and cannot be easily detected from the observed tweets without the aid of machine learning tools.

Microblog sentiment analysis with weak dependency connections

Abstract: With the rise of microblogging services like Twitter and Sina Weibo, users are able to post their real-time mood and opinions conveniently and swiftly. At the same time, the ubiquitous social media results in abundant social relations such as following and follower relations. Social relations create a new source for microblog sentiment analysis, which attracts a great amount of attention in recent years. There are two theories that support the use of social relations for sentiment analysis - sentiment consistency and emotional contagion. However, most existing microblog sentiment analysis methods only employ direct connections which cannot fully use the heterogeneous connections in social media. As online social networks consist of communities and nodes in the same community which form weak dependency connections usually share similarities, we investigate how to exploit weak dependency connections as an aspect of social contexts for microblog sentiment analysis in this paper. In particular, we employ community detection methods to capture weak dependency connections and propose a new model for microblog sentiment analysis which incorporates weak dependency connections, sentiment consistency, and emotional contagion together with text information. Experimental results on two real Twitter datasets demonstrate that our proposed model can outperform baseline methods consistently and significantly.

Student Perceptions of Microblogging: Integrating Twitter with Blogging to Support Learning and Interaction.

Abstract: Social networking technologies are used by millions of individuals around the globe to foster dialogue and share all types of information. It is therefore common to see that campuses abound with students embracing these technologies, sharing everything from personal experiences to general interests and current events with their immediate and extended social circles. Unfortunately, the adaptation of institutional course management system (CMS) software has not been so progressive, and the inclusion of these popular and powerful technologies, which have the potential to foster learning and interaction from a social context, remain somewhat absent from popular CMS platforms, such as Blackboard and Angel.

Exploiting Information Diffusion Feature for Link Prediction in Sina Weibo

Abstract: The rapid development of online social networks (e.g., Twitter and Facebook) has promoted research related to social networks in which link prediction is a key problem. Although numerous attempts have been made for link prediction based on network structure, node attribute and so on, few of the current studies have considered the impact of information diffusion on link creation and prediction. This paper mainly addresses Sina Weibo, which is the largest microblog platform with Chinese characteristics, and proposes the hypothesis that information diffusion influences link creation and verifies the hypothesis based on real data analysis. We also detect an important feature from the information diffusion process, which is used to promote link prediction performance. Finally, the experimental results on Sina Weibo dataset have demonstrated the effectiveness of our methods.

Are Some Tweets More Interesting Than Others? #HardQuestion

Abstract: Twitter has evolved into a significant communication nexus, coupling personal and highly contextual utterances with local news, memes, celebrity gossip, headlines, and other microblogging subgenres. If we take Twitter as a large and varied dynamic collection, how can we predict which tweets will be interesting to a broad audience in advance of lagging social indicators of interest such as retweets? The telegraphic form of tweets, coupled with the subjective notion of interestingness, makes it difficult for human judges to agree on which tweets are indeed interesting.In this paper, we address two questions: Can we develop a reliable strategy that results in high-quality labels for a collection of tweets, and can we use this labeled collection to predict a tweet's interestingness? To answer the first question, we performed a series of studies using crowdsourcing to reach a diverse set of workers who served as a proxy for an audience with variable interests and perspectives. This method allowed us to explore different labeling strategies, including varying the judges, the labels they applied, the datasets, and other aspects of the task. To address the second question, we used crowdsourcing to assemble a set of tweets rated as interesting or not; we scored these tweets using textual and contextual features; and we used these scores as inputs to a binary classifier. We were able to achieve moderate agreement (κ = 0.52) between the best classifier and the human assessments, a figure which reflects the challenges of the judgment task.