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.

Modeling and Predicting Popularity Dynamics of Microblogs using Self-Excited Hawkes Processes

Abstract: The ability to model and predict the popularity dynamics of individual user generated items on online media has important implications in a wide range of areas. In this paper, we propose a probabilistic model using a Self-Excited Hawkes Process (SEHP) to characterize the process through which individual microblogs gain their popularity. This model explicitly captures the triggering effect of each forwarding, distinguishing itself from the reinforced Poisson process based model where all previous forwardings are simply aggregated as a single triggering effect. We validate the proposed model by applying it on Sina Weibo, the most popular microblogging network in China. Experimental results demonstrate that the SEHP model consistently outperforms the model based on reinforced Poisson process.

An unsupervised framework of exploring events on twitter: filtering, extraction and categorization

Abstract: Twitter, as a popular microblogging service, has become a new information channel for users to receive and exchange the most up-to-date information on current events. However, since there is no control on how users can publish messages on Twitter, finding newsworthy events from Twitter becomes a difficult task like "finding a needle in a haystack". In this paper we propose a general unsupervised framework to explore events from tweets, which consists of a pipeline process of filtering, extraction and categorization. To filter out noisy tweets, the filtering step exploits a lexicon-based approach to separate tweets that are event-related from those that are not. Then, based on these event-related tweets, the structured representations of events are extracted and categorized automatically using an unsupervised Bayesian model without the use of any labelled data. Moreover, the categorized events are assigned with the event type labels without human intervention. The proposed framework has been evaluated on over 60 millions tweets which were collected for one month in December 2010. A precision of 70.49% is achieved in event extraction, outperforming a competitive baseline by nearly 6%. Events are also clustered into coherence groups with the automatically assigned event type label.

Character level embedding with deep convolutional neural network for text normalization of unstructured data for Twitter sentiment analysis

Abstract: On social media platforms such as Twitter and Facebook, people express their views, arguments, and emotions of many events in daily life. Twitter is an international microblogging service featuring short messages called “tweets” from different languages. These texts often consist of noise in the form of incorrect grammar, abbreviations, freestyle, and typographical errors. Sentiment analysis (SA) aims to predict the actual emotions from the raw text expressed by the people through the field of natural language processing (NLP). The main aim of our work is to process the raw sentence from the Twitter dataset and find the actual polarity of the message. This paper proposes a text normalization with deep convolutional character level embedding (Conv-char-Emb) neural network model for SA of unstructured data. This model can tackle the problems: (1) processing the noisy sentence for sentiment detection (2) handling small memory space in word level embedded learning (3) accurate sentiment analysis of the unstructured data. The initial preprocessing stage for performing text normalization includes the following steps: tokenization, out of vocabulary (OOV) detection and its replacement, lemmatization and stemming. A character-based embedding in convolutional neural network (CNN) is an effective and efficient technique for SA that uses less learnable parameters in feature representation. Thus, the proposed method performs both the normalization and classification of sentiments for unstructured sentences. The experimental results are evaluated in the Twitter dataset by a different point polarity (positive, negative and neutral). As a result, our model performs well in normalization and sentiment analysis of the raw Twitter data enriched with hidden information.

Extrinsic versus intrinsic motivation in continued twitter usage

Abstract: This research investigates use continuance in the most popular microblogging service, Twitter. Based on TAM and motivational models, we develop an integrated research model to explore the motives that lead users to continued Twitter usage. Structural equation modelling analysis of survey data from 385 Twitter users revealed the salient role of intrinsic motivation and perceived ease of use in continued Twitter usage. Our findings have important implications for theory and practice in this new area of inquiry.

Microblogging To Foster Connections And Community in a Weekly Graduate Seminar Course

Abstract: This study investigated the use of microblogs to enhance content learning and to foster community between participants in a weekly multidisciplinary graduate seminar on teaching and pedagogy. Data included students’ and instructor’s Twitter posts, an initial reaction paper, and an end-of-semester survey to determine if and how students’ attitudes about using Twitter had changed over the course of the semester. A content analysis of the data indicates that students did use Twitter to connect to the content and to each other. Recommendations are given for both instructors and students interested in using social media to extend the college classroom.