12,064 research outputs found
Effectiveness of Corporate Social Media Activities to Increase Relational Outcomes
This study applies social media analytics to investigate the impact of different corporate social media activities on user word of mouth and attitudinal loyalty. We conduct a multilevel analysis of approximately 5 million tweets regarding the main Twitter accounts of 28 large global companies. We empirically identify different social media activities in terms of social media management strategies (using social media management tools or the web-frontend client), account types (broadcasting or receiving information), and communicative approaches (conversational or disseminative). We find positive effects of social media management tools, broadcasting accounts, and conversational communication on public perception
Social media analytics: a survey of techniques, tools and platforms
This paper is written for (social science) researchers seeking to analyze the wealth of social media now available. It presents a comprehensive review of software tools for social networking media, wikis, really simple syndication feeds, blogs, newsgroups, chat and news feeds. For completeness, it also includes introductions to social media scraping, storage, data cleaning and sentiment analysis. Although principally a review, the paper also provides a methodology and a critique of social media tools. Analyzing social media, in particular Twitter feeds for sentiment analysis, has become a major research and business activity due to the availability of web-based application programming interfaces (APIs) provided by Twitter, Facebook and News services. This has led to an ‘explosion’ of data services, software tools for scraping and analysis and social media analytics platforms. It is also a research area undergoing rapid change and evolution due to commercial pressures and the potential for using social media data for computational (social science) research. Using a simple taxonomy, this paper provides a review of leading software tools and how to use them to scrape, cleanse and analyze the spectrum of social media. In addition, it discussed the requirement of an experimental computational environment for social media research and presents as an illustration the system architecture of a social media (analytics) platform built by University College London. The principal contribution of this paper is to provide an overview (including code fragments) for scientists seeking to utilize social media scraping and analytics either in their research or business. The data retrieval techniques that are presented in this paper are valid at the time of writing this paper (June 2014), but they are subject to change since social media data scraping APIs are rapidly changing
Tweeting your Destiny: Profiling Users in the Twitter Landscape around an Online Game
Social media has become a major communication channel for communities
centered around video games. Consequently, social media offers a rich data
source to study online communities and the discussions evolving around games.
Towards this end, we explore a large-scale dataset consisting of over 1 million
tweets related to the online multiplayer shooter Destiny and spanning a time
period of about 14 months using unsupervised clustering and topic modelling.
Furthermore, we correlate Twitter activity of over 3,000 players with their
playtime. Our results contribute to the understanding of online player
communities by identifying distinct player groups with respect to their Twitter
characteristics, describing subgroups within the Destiny community, and
uncovering broad topics of community interest.Comment: Accepted at IEEE Conference on Games 201
Computational Content Analysis of Negative Tweets for Obesity, Diet, Diabetes, and Exercise
Social media based digital epidemiology has the potential to support faster
response and deeper understanding of public health related threats. This study
proposes a new framework to analyze unstructured health related textual data
via Twitter users' post (tweets) to characterize the negative health sentiments
and non-health related concerns in relations to the corpus of negative
sentiments, regarding Diet Diabetes Exercise, and Obesity (DDEO). Through the
collection of 6 million Tweets for one month, this study identified the
prominent topics of users as it relates to the negative sentiments. Our
proposed framework uses two text mining methods, sentiment analysis and topic
modeling, to discover negative topics. The negative sentiments of Twitter users
support the literature narratives and the many morbidity issues that are
associated with DDEO and the linkage between obesity and diabetes. The
framework offers a potential method to understand the publics' opinions and
sentiments regarding DDEO. More importantly, this research provides new
opportunities for computational social scientists, medical experts, and public
health professionals to collectively address DDEO-related issues.Comment: The 2017 Annual Meeting of the Association for Information Science
and Technology (ASIST
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
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