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.

Effectiveness of state-of-the-art features for microblog search

Abstract: We investigate in this paper information retrieval in microblogs exploiting different state-of-the-art features. Microbloggers, besides posting microblogs, search for fresh and relevant information related to their interests, by submitting a query to a microblog search engine. The majority of approaches that collect information from microblogs exploit features such as the recency of the microblog, the authority of his/her author... to improve the quality of their results. In this paper, we evaluated some of the state-of-the-art features to determine those that discriminate relevant from irrelevant microblogs given an information need. Then, we used the selected features to learn models to determine their effectiveness in a microblog search task. We conducted a series of experiments using the dataset and topics of the TREC Microblog 2011 and 2012 tracks. Results show that content, hypertextuality, and recency are the best predictors of relevance. We also found that Naive Bayes was the most effective learning approach for this type of classification.

Multi-modal microblog classification via multi-task learning

Abstract: Recent years have witnessed the flourishing of social media platforms (SMPs), such as Twitter, Facebook, and Sina Weibo. The rapid development of these SMPs has resulted in increasingly large scale multimedia data, which has been proved with remarkable marketing values. It is in an urgent need to classify these social media data into a specified list of concerned entities, such as brands, products, and events, to analyze their sales, popularity or influences. But this is a rather challenging task due to the shortness, conversationality, the incompatibility between images and text, and the data diversity of microblogs. In this paper, we present a multi-modal microblog classification method in a multi-task learning framework. Firstly features of different modalities are extracted for each microblog. Specifically, we extract TF-IDF features for each microblog text and low-level visual features and high-level semantic features for each microblog image. Then multiple related classification tasks are learned simultaneously for each feature to increase the sample size for each task and improve the prediction performance. Finally the outputs of each feature are integrated by a Support Vector Machine that learns how to optimally combine and weight each feature. We evaluate the proposed method on Brand-Social-Net to classify the contained 100 brands. Experimental results demonstrate the superiority of the proposed method, as compared to the state-of-the-art approaches.

Culture and Social Media Usage: Analysis of Japanese Twitter Users

Abstract: Twitter, one of the most popular microblogging tools, has been used extensively all around the world. However, up to date, no study has addressed how culture influences the use of this communication platform. In order to close the literature gap and promote cross-cultural understandings, this paper content analyzed 4,000 tweets from 200 college students in Japan and the USA. The results showed that Japanese college students post more self-related messages and ask fewer questions compared to American college students. It was also found that tweets that refer to TV are more common in Japan, whereas sports and news tweets stand out in the USA. The evidence from this study suggests that there is a subtle and complicated relationship between culture and Twitter use. To cite this document: Adam Acar and Ayaka Deguchi, "Culture and social media usage: Analysis of Japanese twitter users", International Journal of Electronic Commerce Studies, Vol.4, No.1, pp.21-32, 2013. Permanent link to this document: http://dx.doi.org/10.7903/ijecs.989

Finding news-topic oriented influential twitter users based on topic related hashtag community detection

Abstract: Recently, more and more users would like to collect and provide information about news topics in Twitter, which is one of the most popular microblogging services. Virtual communities defined by hashtags in Twitter are created for exchanging information about the news topic. Finding influential Twitter users in these communities related to a news topic would help us understand why some opinions are popular, and get valuable and reliable information for the news topic. In this paper, we propose a new approach to detect news-topic-related user communities defined by hashtags based on characteristic co-occurrence word detection. We also propose RetweetRank and MentionRank to find two types of influential Twitter users from these news-topic-related communities based on user's retweet and mention activities. Experimental results show that our characteristic co-occurrence word detection methods could detect words which are highly relevant to the news topic. RetweetRank could find influential Twitter users whose tweets about the news topic are valuable and more likely to interest others. MentionRank could find influential Twitter users who have high authority on the news topic. Our methods also outperform other related methods in evaluations.

Wikipedia‐based topic clustering for microblogs

Abstract: Microblogging has become a primary channel by which people not only share information, but also search for information. However, microblog search results are most often displayed by simple criteria such as creation time or author. A review of the literature suggests that clustering by topic may be useful, but short posts offer limited scope for clustering using lexical evidence alone. This paper therefore presents an approach to topical clustering based on augmenting lexical evidence with the use of Wikipedia as an external source of evidence for topical similarity. The main idea is to link terms in microblog posts to Wikipedia pages and then to leverage Wikipedia's link structure to estimate semantic similarity, Results show statistically significant relative improvements of about 3% in cluster purity using a relatively small (7500-post, 5-topic) Twitter test collection. Linking terms in microblog posts to Wikipedia pages is also shown to offer a useful basis for cluster labeling.