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.

Performing sentiment analysis in Bangla microblog posts

Abstract: Much of the research work on sentiment analysis has been carried out in the English language, but work in Bangla is limited to only news corpus and blogs. Microblogging sites are becoming a valuable source for publishing huge volumes of user-generated information, as users express their views, opinions, and sentiments over various topics. In this paper, we aim to automatically extract the sentiments or opinions conveyed by users from Bangla microblog posts and then identify the overall polarity of texts as either negative or positive. We use a semi-supervised bootstrapping approach for the development of the training corpus which avoids the need for labor intensive manual annotation. For classification, we use Support Vector Machine (SVM) and Maximum Entropy (MaxEnt) and do a comparative analysis on the performance of these two machine learning algorithms by experimenting with a combination of various sets of features.

Whom to mention: expand the diffusion of tweets by @ recommendation on micro-blogging systems

Abstract: Nowadays, micro-blogging systems like Twitter have become one of the most important ways for information sharing. In Twitter, a user posts a message (tweet) and the others can forward the message (retweet). Mention is a new feature in micro-blogging systems. By mentioning users in a tweet, they will receive notifications and their possible retweets may help to initiate large cascade diffusion of the tweet. To enhance a tweet's diffusion by finding the right persons to mention, we propose in this paper a novel recommendation scheme named as whom-to-mention. Specifically, we present an in-depth study of mention mechanism and propose a recommendation scheme to solve the essential question of whom to mention in a tweet. In this paper, whom-to-mention is formulated as a ranking problem and we try to address several new challenges which are not well studied in the traditional information retrieval tasks. By adopting features including user interest match, content-dependent user relationship and user influence, a machine learned ranking function is trained based on newly defined information diffusion based relevance. The extensive evaluation using data gathered from real users demonstrates the advantage of our proposed algorithm compared with the traditional recommendation methods.

T-SAF: Twitter sentiment analysis framework using a hybrid classification scheme

Abstract: Of the many social media sites available, users prefer microblogging services such as Twitter to learn about product services, social events, and political trends. Twitter is considered an important source of information in sentiment analysis applications. Supervised and unsupervised machine learning-based techniques for Twitter data analysis have been investigated in the last few years, often resulting in an incorrect classification of sentiments. In this paper, we focus on these issues and present a unified framework for classifying tweets using a hybrid classification scheme. The proposed method aims at improving the performance of Twitter-based sentiment analysis systems by incorporating 4 classifiers: (a) a slang classifier, (b) an emoticon classifier, (c) the SentiWordNet classifier, and (d) an improved domain-specific classifier. After applying the preprocessing steps, the input text is passed through the emoticon and slang classifiers. In the next stage, SentiWordNet-based and domain-specific classifiers are applied to classify the text more accurately. Finally, sentiment classification is performed at sentence and document levels. The findings revealed that the proposed method overcomes the limitations of previous methods by considering slang, emoticons, and domain-specific terms.

Places going viral: Twitter usage patterns in destination marketing and place branding

Abstract: Purpose – The purpose of this paper is to analyse how Twitter is utilized by five prominent American destination marketing projects (Illinois, San Francisco, Idaho, Texas, and Milwaukee) to understand the overall trends and usage patterns of microblogging, and the relation of social media ecology and place branding. Design/methodology/approach – This is a comparative study of five Twitter accounts belonging to five destination marketing offices (@enjoyillinois, @onlyinsf, @visitidaho, @texastourism, and @visitmilwaukee). This research looks at two different types of communication activities on Twitter: one-way communication (i.e. broadcasting messages), and two-way communication (i.e. conversing with other users). A total of 5,582 tweets created between October 10, 2011 and October 10, 2012 were analyzed in terms of main topics and subjects covered, and main communication activities engaged. Findings – The research found that destination marketing projects tend to use Twitter pre-dominantly to share about...

What is Tumblr: a statistical overview and comparison

Abstract: Tumblr, as one of the most popular microblogging platforms, has gained momentum recently. It is reported to have 166.4 millions of users and 73.4 billions of posts by January 2014. While many articles about Tumblr have been published in major press, there is not much scholar work so far. In this paper, we provide some pioneer analysis on Tumblr from a variety of aspects. We study the social network structure among Tumblr users, analyze its user generated content, and describe reblogging patterns to analyze its user behavior. We aim to provide a comprehensive statistical overview of Tumblr and compare it with other popular social services, including blogosphere, Twitter and Facebook, in answering a couple of key questions: What is Tumblr? How is Tumblr different from other social media networks? In short, we find Tumblr has more rich content than other microblogging platforms, and it contains hybrid characteristics of social networking, traditional blogosphere, and social media. This work serves as an early snapshot of Tumblr that later work can leverage.