Gaining S-T-E-A-M: A General Athletic Department Social Media Strategy

In the 10 years since the invention of Facebook, social media sites have become an indispensable part of the marketing and communications strategy employed by a broad spectrum of organizations, including university athletic departments. While social media is almost universally used, a review of academic literature suggests the study of deployment of social media resources, and analysis of their effectiveness, is still very much in preliminary stages. Professional literature on social media use is out in front of peer-reviewed research. Therefore, we use Funk’s framework for social media practices as a point of departure, offering a social media strategy specifically for university athletic departments, grounded in Social Marketing Theory. Using a case study of Old Dominion University, a mid-sized, U.S. college athletic department, the authors analyze the 40 social media pages run by the department in comparison to guidelines created from the Funk framework and the growing body of academic literature, conduct interviews with practitioners in the athletic department, and a focus group of fans. Using this data, the authors create a case study-based list of best practices, known by the acronym S-T-E-A-M, which could assist similar university athletic departments in their use of social media

Characterizing Information Diets of Social Media Users

With the widespread adoption of social media sites like Twitter and Facebook, there has been a shift in the way information is produced and consumed. Earlier, the only producers of information were traditional news organizations, which broadcast the same carefully-edited information to all consumers over mass media channels. Whereas, now, in online social media, any user can be a producer of information, and every user selects which other users she connects to, thereby choosing the information she consumes. Moreover, the personalized recommendations that most social media sites provide also contribute towards the information consumed by individual users. In this work, we define a concept of information diet -- which is the topical distribution of a given set of information items (e.g., tweets) -- to characterize the information produced and consumed by various types of users in the popular Twitter social media. At a high level, we find that (i) popular users mostly produce very specialized diets focusing on only a few topics; in fact, news organizations (e.g., NYTimes) produce much more focused diets on social media as compared to their mass media diets, (ii) most users' consumption diets are primarily focused towards one or two topics of their interest, and (iii) the personalized recommendations provided by Twitter help to mitigate some of the topical imbalances in the users' consumption diets, by adding information on diverse topics apart from the users' primary topics of interest.Comment: In Proceeding of International AAAI Conference on Web and Social Media (ICWSM), Oxford, UK, May 201

Validating Network Value of Influencers by means of Explanations

Recently, there has been significant interest in social influence analysis. One of the central problems in this area is the problem of identifying influencers, such that by convincing these users to perform a certain action (like buying a new product), a large number of other users get influenced to follow the action. The client of such an application is a marketer who would target these influencers for marketing a given new product, say by providing free samples or discounts. It is natural that before committing resources for targeting an influencer the marketer would be interested in validating the influence (or network value) of influencers returned. This requires digging deeper into such analytical questions as: who are their followers, on what actions (or products) they are influential, etc. However, the current approaches to identifying influencers largely work as a black box in this respect. The goal of this paper is to open up the black box, address these questions and provide informative and crisp explanations for validating the network value of influencers. We formulate the problem of providing explanations (called PROXI) as a discrete optimization problem of feature selection. We show that PROXI is not only NP-hard to solve exactly, it is NP-hard to approximate within any reasonable factor. Nevertheless, we show interesting properties of the objective function and develop an intuitive greedy heuristic. We perform detailed experimental analysis on two real world datasets - Twitter and Flixster, and show that our approach is useful in generating concise and insightful explanations of the influence distribution of users and that our greedy algorithm is effective and efficient with respect to several baselines

Followee recommendation based on text analysis of micro-blogging activity

Nowadays, more and more users keep up with news through information streams coming from real-time micro-blogging activity offered by services such as Twitter. In these sites, information is shared via a followers/followees social network structure in which a follower receives all the micro-blogs from his/her followees. Recent research efforts on understanding micro-blogging as a novel form of communication and news spreading medium, have identified three different categories of users in these systems: information sources, information seekers and friends. As social networks grow in the number of registered users, finding relevant and reliable users to receive interesting information becomes essential. In this paper we propose a followee recommender system based on both the analysis of the content of micro-blogs to detect users´ interests and in the exploration of the topology of the network to find candidate users for recommendation. Experimental evaluation was conducted in order to determine the impact of different profiling strategies based on the text analysis of micro-blogs as well as several factors that allows the identification of users acting as good information sources. We found that user-generated content available in the network is a rich source of information for profiling users and finding like-minded people.Fil: Armentano, Marcelo Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Godoy, Daniela Lis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Amandi, Analia Adriana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentin

How open are journalists on Twitter? Trends towards the end-user journalism

The many activities of journalists on Twitter should be analyzed. Are they doing a different kind of journalism? With a content analysis of 1125 tweets, this study reveals trends of some Spanish journalists using Twitter. A traditional role like gatekeeping can be highly amplified in terms of transparency and accountability with actions as retweeting or linking. The landscape offered by this platform is framed with the "ambient journalism", which will help to understand the proposal of this study: the end-user journalism. The findings will show the level of opening with the audience in aspects about replies, requests and linking

The National Dialogue on the Quadrennial Homeland Security Review

Six years after its creation, the Department of Homeland Security (DHS) undertook the first Quadrennial Homeland Security Review (QHSR) to inform the design and implementation of actions to ensure the safety of the United States and its citizens. This review, mandated by the Implementing the 9/11 Commission Recommendations Act of 2007, represents the first comprehensive examination of the homeland security strategy of the nation. The QHSR includes recommendations addressing the long-term strategy and priorities of the nation for homeland security and guidance on the programs, assets, capabilities, budget, policies, and authorities of the department.Rather than set policy internally and implement it in a top-down fashion, DHS undertook the QHSR in a new and innovative way by engaging tens of thousands of stakeholders and soliciting their ideas and comments at the outset of the process. Through a series of three-week-long, web-based discussions, stakeholders reviewed materials developed by DHS study groups, submitted and discussed their own ideas and priorities, and rated or "tagged" others' feedback to surface the most relevant ideas and important themes deserving further consideration.Key FindingsThe recommendations included: (1) DHS should enhance its capacity for coordinating stakeholder engagement and consultation efforts across its component agencies, (2) DHS and other agencies should create special procurement and contracting guidance for acquisitions that involve creating or hosting such web-based engagement platforms as the National Dialogue, and (3) DHS should begin future stakeholder engagements by crafting quantitative metrics or indicators to measure such outcomes as transparency, community-building, and capacity

Bridging recommendation and adaptation:generic adaptation framework - twittomender compliance study

In this paper we consider Recommender System (RS) modeling in terms of Adaptive Hypermedia Systems (AHS) and investigate AHS and RS functionality compliance in terms of common features, functionality, building blocks and composition of the system. We bring up complementary aspects of adaptation, personalization and recommendation in a context of a generic framework which provides properties of information fusion and heterogeneity and could serve as a reference model. We show major recommendation functionality in terms of the reference structure and recommendation process by presenting a conceptual generic ‘adaptation-recommendation’ sequence chart which overlays and combines properties of adaptation and recommendations taking advantages of both. In fact we show that RS if implemented on the web can be considered as AHS, in this wise a generic framework should be capable of describing virtually any RS. In the case study we scrutinize the Twittomender3 RS. We decompose the system in building blocks, outline and highlight its properties along with the advantages and possible enhancements. We conclude by summarizing framework advantages and AH recommendation compliant features as well as lessons learned from this study

Motivations of User Engagement in eWOM of Chinese Microblog

This study examined the motivations of user engagement in electronic word-of-mouth behavior through a netnography. The sample includes more than 2,000 posts from the Chinese microblog-Sina Weibo and 20 follow-up interviews about film reviews. 5 types of motivations were examined, including egoistic motivations, motivations for the reward, reciprocal motivations, altruistic motivations, and motivations in the sense of community. Results confirmed the effect of egoistic motivations, reciprocal motivations, and motivations in the sense of community for the increasing volume of eWOM. However, motivations for the reward and altruistic motivations were found less powerful. Hence, the reward was found to decrease the generation of eWOM.The findings would give implications for marketers to plan and leverage eWoM and help scholars understand eWoM in a microblog context