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.

Evaluating Real-Time Search over Tweets

Abstract: Twitter offers a phenomenal platform for the social sharing of information. We describe new resources that have been created in the context of the Text Retrieval Conference (TREC) to support the academic study of Twitter as a real-time information source. We formalize an information seeking task — real-time search — and offer a methodology for measuring system effectiveness. At the TREC 2011 Microblog Track, 58 research groups participated in the first ever evaluation of this task. We present data from the effort to illustrate and support our methodology.

The potential of microblogs for the study of public perceptions of climate change

Abstract: Microblogs are electronic platforms that convey brief communications posted by users. Keyword searches in popular microblogs, like Twitter, reveal fragments of users' knowledge of and views on issues like climate change. Evaluations of climate change communications in the microblogosphere are rare even compared with the few studies on the impacts of Web sites and blogs on users' perceptions of climate change. However, extant research focuses more often on appraising logic and evidence in microblog discourse than in discovering pathways of influence and impact. The limiting ‘frames’ imposed by strategic users of microblogs and the persuasive power of ‘influencers’ are often depicted as interfering with the open, egalitarian potential of microblogs, and also, as perpetrating bias and misinformation. But oversimplifying or biased framings and pronouncements by celebrities are the stock and trade of microblogs. Good or bad, they are part of a communication medium whose users plunge in to exchange views, to persuade, and to be persuaded. Tweets and posts on any number of issues are fodder for attitudinal analytics and predictive modelling. Tools of the analytical trade should be applied to climate change microblogging, too, considering the sheer number of people who post commentary on this topic, and considering the continuing need to better understand how people view and engage with climate change. For further resources related to this article, please visit the WIREs website. Conflict of interest: The authors have declared no conflicts of interest for this article.

Forecasting with social media: evidence from tweets on soccer matches

Abstract: Social media is now used as a forecasting tool by a variety of firms and agencies. But how useful are such data in forecasting outcomes? Can social media add any information to that produced by a prediction/betting market? We source 13.8 million posts from Twitter, and combine them with contemporaneous Betfair betting prices, to forecast the outcomes of English Premier League soccer matches as they unfold. Using a microblogging dictionary to analyze the content of Tweets, we find that the aggregate tone of Tweets contains significant information not in betting prices, particularly in the immediate aftermath of goals and red cards. (JEL G14, G17)

Extracting situational awareness from microblogs during disaster events

Abstract: Microblogging sites such as Twitter and Weibo are increasingly being used to enhance situational awareness during various natural and man-made disaster events such as floods, earthquakes, and bomb blasts. During any such event, thousands of microblogs (tweets) are posted in short intervals of time. Typically, only a small fraction of these tweets contribute to situational awareness, while the majority merely reflect the sentiment or opinion of people. Real-time extraction of tweets that contribute to situational awareness is especially important for relief operations when time is critical. However, automatically differentiating such tweets from those that reflect opinion / sentiment is a non-trivial challenge, mainly because of the very small size of tweets and the informal way in which tweets are written (frequent use of emoticons, abbreviations, and so on). This study applies Natural Language Processing (NLP) techniques to address this challenge. We extract low-level syntactic features from the text of tweets, such as the presence of specific types of words and parts-of-speech, to develop a classifier to distinguish between tweets which contribute to situational awareness and tweets which do not. Experiments over tweets related to four diverse disaster events show that the proposed features identify situational awareness tweets with significantly higher accuracy than classifiers based on standard bag-of-words models.