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.

Event Classification in Microblogs via Social Tracking

Abstract: Social media websites have become important information sharing platforms. The rapid development of social media platforms has led to increasingly large-scale social media data, which has shown remarkable societal and marketing values. There are needs to extract important events in live social media streams. However, microblogs event classification is challenging due to two facts, i.e., the short/conversational nature and the incompatible meanings between the text and the corresponding image in social posts, and the rapidly evolving contents. In this article, we propose to conduct event classification via deep learning and social tracking. First, we introduce a Multi-modal Multi-instance Deep Network (M2DN) for microblogs classification, which is able to handle the weakly labeled microblogs data oriented from the incompatible meanings inside microblogs. Besides predicting each microblogs as predefined events, we propose to employ social tracking to extract social-related auxiliary information to enrich the testing samples. We extract a set of candidate-relevant microblogs in a short time window by using social connections, such as related users and geographical locations. All these selected microblogs and the testing data are formulated in a Markov Random Field model. The inference on the Markov Random Field is conducted to update the classification results of the testing microblogs. This method is evaluated on the Brand-Social-Net dataset for classification of 20 events. Experimental results and comparison with the state of the arts show that the proposed method can achieve better performance for the event classification task.

Sentiment Analysis using Machine Learning and Deep Learning

Abstract: With the increasing rate at which data is created by internet users on various platforms, it becomes necessary to analyze and make use of the data by the Defense and other Government Organizations and know the sentiment of the people. This shall help the organizations take control of their actions and decide the steps to be taken shortly. Added to it, when something crucial is happening in the nation, it is of paramount importance to decide every step without hurting/violating the sentiments of the people. In the era of Microblogging, which has become quite a popular tool of communication, millions of users share their views and opinions on various day-to-day life issues concerning them directly or indirectly through social media platforms like Twitter, Reddit, Tumblr, Facebook. Data from these sites can be efficiently used for marketing or social studies. In this paper, we have taken into account various methods to perform sentiment analysis. Sentiment Analysis has been performed by using Machine Learning Classifiers. Polarity-based sentiment analysis, and Deep Learning Models are used to classify user's tweets as having ‘positive’ or ‘negative’ sentiment. The idea behind taking in various model architectures was to account for the variance in the opinions and thoughts existing on such social media platforms. These classification models can further be implemented to classify live tweets on twitter on any topic.

Leveraging Behavioral and Social Information for Weakly Supervised Collective Classification of Political Discourse on Twitter

Abstract: Framing is a political strategy in which politicians carefully word their statements in order to control public perception of issues. Previous works exploring political framing typically analyze frame usage in longer texts, such as congressional speeches. We present a collection of weakly supervised models which harness collective classification to predict the frames used in political discourse on the microblogging platform, Twitter. Our global probabilistic models show that by combining both lexical features of tweets and network-based behavioral features of Twitter, we are able to increase the average, unsupervised F1 score by 21.52 points over a lexical baseline alone.

Using Location-Based Social Media Data to Observe Check-In Behavior and Gender Difference: Bringing Weibo Data into Play

Abstract: Population density and distribution of services represents the growth and demographic shift of the cities. For urban planners, population density and check-in behavior in space and time are vital factors for planning and development of sustainable cities. Location-based social network (LBSN) data seems to be a complement to many traditional methods (i.e., survey, census) and is used to study check-in behavior, human mobility, activity analysis, and social issues within a city. This check-in phenomenon of sharing location, activities, and time by users has encouraged this research on gender difference and frequency of using LBSN. Therefore, in this study, we investigate the check-in behavior of Chinese microblog Sina Weibo (referred as " Weibo ") in 10 districts of Shanghai, China, for which we observe the gender difference and their frequency of use over a period. The mentioned districts were spatially analyzed for check-in spots by kernel density estimation (KDE) using ArcGIS. Furthermore, our results reveal that female users have a high rate of social media use, and significant difference is observed in check-in behavior during weekdays and weekends in the studied districts of Shanghai. Increase in check-ins is observed during the night as compared to the morning. From the results, it can be assumed that LBSN data can be helpful to observe gender difference.

Microblogging in Open Source Software Development: The Case of Drupal and Twitter

Abstract: Microblogging is a popular form of social media that has quickly permeated both enterprise and open source communities. However, exactly how open source communities can leverage microblogging isn't yet well understood. The authors investigate how Drupal's open source community uses Twitter, a household name in microblogging. Their analysis of group and individual accounts of Drupal developers reveals that they take on similar but distinct roles. Both serve as communicators of essential links to a vast and growing community knowledge base, such as work artifacts, issues, documentation, and blog posts, but community members often express positive emotions when tweeting about work, which reinforces a sense of community.