Mainstream media behavior analysis on Twitter:a case study on UK general election

With the development of social media tools such as Facebook and Twitter, mainstream media organizations including newspapers and TV media have played an active role in engaging with their audience and strengthening their influence on the recently emerged platforms. In this paper, we analyze the behavior of mainstream media on Twitter and study how they exert their influence to shape public opinion during the UK's 2010 General Election. We first propose an empirical measure to quantify mainstream media bias based on sentiment analysis and show that it correlates better with the actual political bias in the UK media than the pure quantitative measures based on media coverage of various political parties. We then compare the information diffusion patterns from different categories of sources. We found that while mainstream media is good at seeding prominent information cascades, its role in shaping public opinion is being challenged by journalists since tweets from them are more likely to be retweeted and they spread faster and have longer lifespan compared to tweets from mainstream media. Moreover, the political bias of the journalists is a good indicator of the actual election results. Copyright 2013 ACM.

Predicting answering behaviour in online question answering communities

The value of Question Answering (Q&A) communities is dependent on members of the community finding the questions they are most willing and able to answer. This can be difficult in communities with a high volume of questions. Much previous has work attempted to address this problem by recommending questions similar to those already answered. However, this approach disregards the question selection behaviour of the answers and how it is affected by factors such as question recency and reputation. In this paper, we identify the parameters that correlate with such a behaviour by analysing the users' answering patterns in a Q&A community. We then generate a model to predict which question a user is most likely to answer next. We train Learning to Rank (LTR) models to predict question selections using various user, question and thread feature sets. We show that answering behaviour can be predicted with a high level of success, and highlight the particular features that inuence users' question selections.

Emerging perspectives on the design, use, and evaluation of mobile and handheld devices

Human-computer interaction is a growing field of study in which researchers and professionals aim to understand and evaluate the impact of new technologies on human behavior. With the integration of smart phones, tablets, and other portable devices into everyday life, there is a greater need to understand the influence of such technology on the human experience. Emerging Perspectives on the Design, Use, and Evaluation of Mobile and Handheld Devices is an authoritative reference source consisting of the latest scholarly research and theories from international experts and professionals on the topic of human-computer interaction with mobile devices. Featuring a comprehensive collection of chapters on critical topics in this dynamic field, this publication is an essential reference source for researchers, educators, students, and practitioners interested in the use of mobile and handheld devices and their impact on individuals and society as a whole. This publication features timely, research-based chapters pertaining to topics in the design and evaluation of smart devices including, but not limited to, app stores, category-based interfaces, gamified mobility applications, mobile interaction, mobile learning, pervasive multimodal applications, smartphone interaction, and social media use.

Pub crawling at scale:tapping untappd to explore social drinking

There has been a recent surge of research looking at the reporting of food consumption on social media. The topic of alcohol consumption, however, remains poorly investigated. Social media has the potential to shed light on a topic that, traditionally, is difficult to collect fine-grained information on. One social app stands out in this regard: Untappd is an app that allows users to ‘check-in’ their consumption of beers. It operates in a similar fashion to other location-based applications, but is specifically tailored to the collection of information on beer consumption. In this paper, we explore beer consumption through the lens of social media. We crawled Untappd in real time over a period of 112 days, across 40 cities in the United States and Europe. Using this data, we shed light on the drinking habits of over 369k users. We focus on per-user and per-city characterisation, highlighting key behavioural trends.