Data Portraits and Intermediary Topics: Encouraging Exploration of Politically Diverse Profiles

In micro-blogging platforms, people connect and interact with others. However, due to cognitive biases, they tend to interact with like-minded people and read agreeable information only. Many efforts to make people connect with those who think differently have not worked well. In this paper, we hypothesize, first, that previous approaches have not worked because they have been direct -- they have tried to explicitly connect people with those having opposing views on sensitive issues. Second, that neither recommendation or presentation of information by themselves are enough to encourage behavioral change. We propose a platform that mixes a recommender algorithm and a visualization-based user interface to explore recommendations. It recommends politically diverse profiles in terms of distance of latent topics, and displays those recommendations in a visual representation of each user's personal content. We performed an "in the wild" evaluation of this platform, and found that people explored more recommendations when using a biased algorithm instead of ours. In line with our hypothesis, we also found that the mixture of our recommender algorithm and our user interface, allowed politically interested users to exhibit an unbiased exploration of the recommended profiles. Finally, our results contribute insights in two aspects: first, which individual differences are important when designing platforms aimed at behavioral change; and second, which algorithms and user interfaces should be mixed to help users avoid cognitive mechanisms that lead to biased behavior.Comment: 12 pages, 7 figures. To be presented at ACM Intelligent User Interfaces 201

Identifying Influential Users Of Micro-Blogging Services: A Dynamic Action-Based Network Approach

In this paper, we present a dynamic model to identify influential users of micro-blogging services. Micro-blogging services, such as Twitter, allow their users (twitterers) to publish tweets and choose to follow other users to receive tweets. Previous work on user influence on Twitter, concerns more on following link structure and the contents user published, seldom emphasizes the importance of interactions among users. We argue that, by emphasizing on user actions in micro-blogging platform, user influence could be measured more accurately. Since micro-blogging is a powerful social media and communication platform, identifying influential users according to user interactions has more practical meanings, e.g., advertisers may concern how many actions – buying, in this scenario – the influential users could initiate rather than how many advertisements they spread. By introducing the idea of PageRank algorithm, innovatively, we propose our model using action-based network which could capture the ability of influential users when they interacting with micro-blogging platform. Taking the evolving prosperity of micro-blogging into consideration, we extend our action-based user influence model into a dynamic one, which could distinguish influential users in different time periods. Simulation results demonstrate that our models could support and give reasonable explanations for the scenarios that we considered

ACQR: A Novel Framework to Identify and Predict Influential Users in Micro-Blogging

As key roles of online social networks, influential users in micro-blogging have the ability to influence the attitudes or behaviour of others. When it comes to marketing, the users’ influence should be associated with a certain topic or field on which people have different levels of preference and expertise. In order to identify and predict influential users in a specific topic more effectively, users’ actual influential capability on a certain topic and potential influence unlimited by topics is combined into a novel comprehensive framework named “ACQR” in this research. ACQR framework depicts the attributes of the influentials from four aspects, including activeness (A), centrality (C), quality of post (Q) and reputation (R). Based on this framework, a data mining method is developed for discovering and forecasting the top influentials. Empirical results reveal that our ACQR framework and the data mining method by TOPSIS and SVMs (with polynomial and RBF kernels) can perform very well in identifying and predicting influential users in a certain topic (such as iPhone 5). Furthermore, the dynamic change processes of users’ influence from longitudinal perspective are analysed and suggestions to the sales managers are provided

Making noise : how Twitter is revolutionizing the art of communication

textThis paper seeks to understand how the micro-blogging tool, Twitter, can be used effectively by marketers and advertisers by incorporating the attributes of information quality, information usefulness and source credibility into their tweets. Case analyses of five of the most popular Twitter accounts were conducted with regards to the appearance of each account and the content of their tweets. Recommendations for improving the quality of each Twitter account were also provided.Advertisin

$1.00 per RT #BostonMarathon #PrayForBoston: analyzing fake content on Twitter

This study found that 29% of the most viral content on Twitter during the Boston bombing crisis were rumors and fake content.AbstractOnline social media has emerged as one of the prominent channels for dissemination of information during real world events. Malicious content is posted online during events, which can result in damage, chaos and monetary losses in the real world. We analyzed one such media i.e. Twitter, for content generated during the event of Boston Marathon Blasts, that occurred on April, 15th, 2013. A lot of fake content and malicious profiles originated on Twitter network during this event. The aim of this work is to perform in-depth characterization of what factors influenced in malicious content and profiles becoming viral. Our results showed that 29% of the most viral content on Twitter, during the Boston crisis were rumors and fake content; while 51% was generic opinions and comments; and rest was true information. We found that large number of users with high social reputation and verified accounts were responsible for spreading the fake content. Next, we used regression prediction model, to verify that, overall impact of all users who propagate the fake content at a given time, can be used to estimate the growth of that content in future. Many malicious accounts were created on Twitter during the Boston event, that were later suspended by Twitter. We identified over six thousand such user profiles, we observed that the creation of such profiles surged considerably right after the blasts occurred. We identified closed community structure and star formation in the interaction network of these suspended profiles amongst themselves

140 CHARACTERS TO SKINNY: SOCIAL SUPPORT PROVIDED BY COMMERCIAL WEIGHT-LOSS PROGRAMS VIA TWITTER

The state of healthcare in the United States is changing. Amidst this change, there is a debate as to whether health is a public good or if health is a private matter. This change and debate challenges health professionals to rethink the way we go about planning health interventions to better address the many public health issues looming the general population today. The recent rise of obesity and at-risk weight in the United States is a major epidemic that has yet to be resolved. There are many approaches to addressing weight management and for many, public health campaigns have not supported lasting behavioral change. Some have looked to the online scale and contracted the support services of popular commercial weight-loss programs for help attaining an ideal weight. Commercial diet and weight-loss programs such as Weight Watchers and Jenny Craig have integrated new media into their communication strategy. These programs have the capacity to support the needs of a wide range of participants while providing customized support through a variety of media. This study focuses on how commercial weight-loss programs use Twitter to facilitate support. This study seeks to better understand how three major commercial weight-loss programs use Twitter as a starting point for future research discovering industry best practices. Through analysis using an adapted version of the Social Support Behavioral Codes (SSBC) - originally employed by Curtron\u27s and Surh (1992) - this study was able to make sense of commercial weight-loss program Twitter use. The results of a content analysis of program tweets (n=1,172) show potential for Twitter to be a valuable tool when integrated into health campaigns. Results also show that commercial weight-loss programs were able to provide all five major themes of support described in the SSBC. Informational support and network support were the predominant themes used by the programs, but the programs did not use the themes of support the same way. The results reported from this analysis make way for future studies in the effectiveness of commercial weight-loss program support. Eventually, these findings will lead to greater practical application of best practices for providing support to those suffering from various diseases using Twitter