Is That Twitter Hashtag Worth Reading

Online social media such as Twitter, Facebook, Wikis and Linkedin have made a great impact on the way we consume information in our day to day life. Now it has become increasingly important that we come across appropriate content from the social media to avoid information explosion. In case of Twitter, popular information can be tracked using hashtags. Studying the characteristics of tweets containing hashtags becomes important for a number of tasks, such as breaking news detection, personalized message recommendation, friends recommendation, and sentiment analysis among others. In this paper, we have analyzed Twitter data based on trending hashtags, which is widely used nowadays. We have used event based hashtags to know users' thoughts on those events and to decide whether the rest of the users might find it interesting or not. We have used topic modeling, which reveals the hidden thematic structure of the documents (tweets in this case) in addition to sentiment analysis in exploring and summarizing the content of the documents. A technique to find the interestingness of event based twitter hashtag and the associated sentiment has been proposed. The proposed technique helps twitter follower to read, relevant and interesting hashtag.Comment: 10 pages, 6 figures, Presented at the Third International Symposium on Women in Computing and Informatics (WCI-2015

Social Information Processing in Social News Aggregation

The rise of the social media sites, such as blogs, wikis, Digg and Flickr among others, underscores the transformation of the Web to a participatory medium in which users are collaboratively creating, evaluating and distributing information. The innovations introduced by social media has lead to a new paradigm for interacting with information, what we call 'social information processing'. In this paper, we study how social news aggregator Digg exploits social information processing to solve the problems of document recommendation and rating. First, we show, by tracking stories over time, that social networks play an important role in document recommendation. The second contribution of this paper consists of two mathematical models. The first model describes how collaborative rating and promotion of stories emerges from the independent decisions made by many users. The second model describes how a user's influence, the number of promoted stories and the user's social network, changes in time. We find qualitative agreement between predictions of the model and user data gathered from Digg.Comment: Extended version of the paper submitted to IEEE Internet Computing's special issue on Social Searc

Fine-grained Video Attractiveness Prediction Using Multimodal Deep Learning on a Large Real-world Dataset

Nowadays, billions of videos are online ready to be viewed and shared. Among an enormous volume of videos, some popular ones are widely viewed by online users while the majority attract little attention. Furthermore, within each video, different segments may attract significantly different numbers of views. This phenomenon leads to a challenging yet important problem, namely fine-grained video attractiveness prediction. However, one major obstacle for such a challenging problem is that no suitable benchmark dataset currently exists. To this end, we construct the first fine-grained video attractiveness dataset, which is collected from one of the most popular video websites in the world. In total, the constructed FVAD consists of 1,019 drama episodes with 780.6 hours covering different categories and a wide variety of video contents. Apart from the large amount of videos, hundreds of millions of user behaviors during watching videos are also included, such as "view counts", "fast-forward", "fast-rewind", and so on, where "view counts" reflects the video attractiveness while other engagements capture the interactions between the viewers and videos. First, we demonstrate that video attractiveness and different engagements present different relationships. Second, FVAD provides us an opportunity to study the fine-grained video attractiveness prediction problem. We design different sequential models to perform video attractiveness prediction by relying solely on video contents. The sequential models exploit the multimodal relationships between visual and audio components of the video contents at different levels. Experimental results demonstrate the effectiveness of our proposed sequential models with different visual and audio representations, the necessity of incorporating the two modalities, and the complementary behaviors of the sequential prediction models at different levels.Comment: Accepted by WWW 2018 The Big Web Trac

Which User-generated Content Should Be Appreciated More? - A Study on UGC Features, Consumers\u27 Behavioral Intentions and Social Media Engagement

Despite researchers have made a great deal of effort on exploring the reasons of travel consumers’ participation in UGC sites and the roles of these sites in different phases of their travel, knowledge on what factors influence travel consumers’ behavioral intentions in social media still remains largely unknown to both scholars and practitioners. With the attempts to find out this, we conducted a two-phase study on Chinese consumers. Utilizing the two sets of data we collected (npost = 65; nratings = 1668), we develop a multiple linear regression model to assess the influential factors in UGC sites on consumers’ behavioral intentions. Our results indicate that travel consumers’ purchase intention, word-of-mouth (WOM) intention, and attitudes towards destination brands are positively affected by the UGC features (creditability and interestingness) and consumers’ social media engagement (comment, retweet, and like).Further, inconsistent with the previous finding that credibility is a major concern in consumers’ information search processes, the interestingness of UGC is found to be more important

Exploring the Influence of User-Generated Content Factors on the Behavioral Intentions of Travel Consumers

Social media have been deemed as more and more critical to modern travel consumers. These consumers often regard social media as trustworthy source that can lower the perceived risk and uncertainty throughout their travel. Though previous studies revealed that travel consumers’ participation in social media could be explained by their functional, social-psychological and hedonic needs, the factors that impact their behavioral intentions, such as purchase intention, WOM intention, and attitudes of destination brands have not been well studied. By conducting a two-phase study on Chinese travel consumers (nposts =65; nratings=1668), we found that both the UGC features (credibility and interestingness) and the social media engagement of travel consumers (comment, retweet, like) can impact their behavioral intentions. In addition, compared to the credibility of a post, the interestingness could more positively influence the social media engagement of travel consumers. Our study gives a better understanding of connections between social media and the travel consumers’ behavioral intentions

A Referral Rewards Incentive Dedign On Travel Consumer- Generated Content

User-generated content has become increasingly important to both tourism practitioners and travel consumers. Although prior studies have demonstrated how impactful UGC is and why marketing mavens employ UGC sites in their marketing campaigns, there is still scant evidence on how to successfully manipulate them. To fill this void, we conducted a two-phase experiment study. In the experiment, first, 65 tourists were invited then grouped according to three different treatments (namely, creating travel posts to achieve the maximum ‘comments’, ‘retweets’, or ‘likes’), and one will be rewarded if he/she achieves the goal. Second, for the manipulation check, we invited another group of Chinese consumers (n =268) to rate these travel posts based on their perceptions. Our experiment results indicate that this referral rewards incentive design has significant effects on consumers’ UGC perception (the credibility, interestingness, influence of postings), behavioral intentions (purchase intention, and WOM intention), and their likelihood of social media engagement (offering ‘likes’). In addition, we also discuss the implications of the results and how to exploit this design

The design and study of pedagogical paper recommendation

For learners engaging in senior-level courses, tutors in many cases would like to pick some articles as supplementary reading materials for them each week. Unlike researchers ‘Googling’ papers from the Internet, tutors, when making recommendations, should consider course syllabus and their assessment of learners along many dimensions. As such, simply ‘Googling’ articles from the Internet is far from enough. That is, learner models of each individual, including their learning interest, knowledge, goals, etc. should be considered when making paper recommendations, since the recommendation should be carried out so as to ensure that the suitability of a paper for a learner is calculated as the summation of the fitness of the appropriateness of it to help the learner in general. This type of the recommendation is called a Pedagogical Paper Recommender.In this thesis, we propose a set of recommendation methods for a Pedagogical Paper Recommender and study the various important issues surrounding it. Experimental studies confirm that making recommendations to learners in social learning environments is not the same as making recommendation to users in commercial environments such as Amazon.com. In such learning environments, learners are willing to accept items that are not interesting, yet meet their learning goals in some way or another; learners’ overall impression towards each paper is not solely dependent on the interestingness of the paper, but also other factors, such as the degree to which the paper can help to meet their ‘cognitive’ goals.It is also observed that most of the recommendation methods are scalable. Although the degree of this scalability is still unclear, we conjecture that those methods are consistent to up to 50 papers in terms of recommendation accuracy. The experiments conducted so far and suggestions made on the adoption of recommendation methods are based on the data we have collected during one semester of a course. Therefore, the generality of results needs to undergo further validation before more certain conclusion can be drawn. These follow up studies should be performed (ideally) in more semesters on the same course or related courses with more newly added papers. Then, some open issues can be further investigated. Despite these weaknesses, this study has been able to reach the research goals set out in the proposed pedagogical paper recommender which, although sounding intuitive, unfortunately has been largely ignored in the research community. Finding a ‘good’ paper is not trivial: it is not about the simple fact that the user will either accept the recommended items, or not; rather, it is a multiple step process that typically entails the users navigating the paper collections, understanding the recommended items, seeing what others like/dislike, and making decisions. Therefore, a future research goal to proceed from the study here is to design for different kinds of social navigation in order to study their respective impacts on user behavior, and how over time, user behavior feeds back to influence the system performance

Breaking the News: First Impressions Matter on Online News

A growing number of people are changing the way they consume news, replacing the traditional physical newspapers and magazines by their virtual online versions or/and weblogs. The interactivity and immediacy present in online news are changing the way news are being produced and exposed by media corporations. News websites have to create effective strategies to catch people's attention and attract their clicks. In this paper we investigate possible strategies used by online news corporations in the design of their news headlines. We analyze the content of 69,907 headlines produced by four major global media corporations during a minimum of eight consecutive months in 2014. In order to discover strategies that could be used to attract clicks, we extracted features from the text of the news headlines related to the sentiment polarity of the headline. We discovered that the sentiment of the headline is strongly related to the popularity of the news and also with the dynamics of the posted comments on that particular news.Comment: The paper appears in ICWSM 201