Social informatics

5th International Conference, SocInfo 2013, Kyoto, Japan, November 25-27, 2013, Proceedings</p

Semantic Wide and Deep Learning for Detecting Crisis-Information Categories on Social Media

When crises hit, many flog to social media to share or consume information related to the event. Social media posts during crises tend to provide valuable reports on affected people, donation offers, help requests, advice provision, etc. Automatically identifying the category of information (e.g., reports on affected individuals, donations and volunteers) contained in these posts is vital for their efficient handling and consumption by effected communities and concerned organisations. In this paper, we introduce Sem-CNN; a wide and deep Convolutional Neural Network (CNN) model designed for identifying the category of information contained in crisis-related social media content. Unlike previous models, which mainly rely on the lexical representations of words in the text, the proposed model integrates an additional layer of semantics that represents the named entities in the text, into a wide and deep CNN network. Results show that the Sem-CNN model consistently outperforms the baselines which consist of statistical and non-semantic deep learning models

Evaluation of Push Notifications for Social Media Applications

The growth of social media has impacted on people’s everyday life, precipitating the development of a new set of guidelines for designing applications (apps), creating heightened user engagement without crossing the line to frustration. This study focuses on how push notifications from social media apps should be designed in order to keep the user intrigued and returning to the app, without annoying the user to the point where they turn the push notifications off. The exponential growth in the usage of social media has emphasised the importance of designing apps with a user- centred functionality. The study used a combination of a survey questionnaire and a qualitative perception study, with the results collected as both data and extracts from interviews. This study identified that a high frequency of notifications from social media apps has led to resentment by users against pushes notifications in general. The app-user relationship is cemented from the beginning of the experience and the action the user takes in relation to notifications depends on their perception of the senders’ intentions. Younger users’ actions are also predominately driven by the phenomena Fear of Missing Out

Beyond Big Bird, Binders, and Bayonets: Humor and Visibility Among Connected Viewers of the 2012 US Presidential Debates

During the 2012 US presidential debates, more than five million connected viewers turned to social media to respond to the broadcast and talk politics with one another. Using a mixed-methods approach, this study examines the prevalence of humor and its relationship to visibility among connected viewers live-tweeting the debates. Based on a content analysis of tweets and accounts, we estimate that approximately one-fifth of the messages sent during the debates consisted of strictly humorous content. Using retweet frequency as a proxy for visibility, we found a positive relationship between the use of humor and the visibility of individual tweets. Not only was humor widespread in the discourse of connected viewers, but humorous messages enjoyed greater overall visibility. These findings suggest a strategic use of humor by political actors seeking greater shares of attention on social media

Listening in: Investigating Social Media Activity in the Streaming Service Industry

In this paper, we examine the social media activity surrounding three different brands (Hulu, Netflix, and Disney+) using two different and complimentary techniques. In Study 1, we use a popular social listening tool to examine quantitative data of different kinds including the share of voice of these brands as well as the major geographic markets and languages associated with these brands\u27 social media activity. These are three of the biggest brands in the over-the-top (OTT) industry and all three of these companies offer streaming services that are highly popular with consumers around the world. To get a better sense of the quantity and quality of the social media posts around these brands, we gathered and studied Twitter data for a four-week period using the Awario social listening tool. Building on this analysis, we then conduct a qualitative analysis of each brand\u27s social media activity using a netnographic, qualitative content analysis of branded social media posts that occurred during the aforementioned 4-week observation period in April 2020. This thesis begins with a literature review that focuses on the larger issue of big data and examines the various tools and techniques that firms use to interpret and act on their big data resources, especially social media posts by their fans and customers. We then move to a brief overview of the OTT industry to provide context for the data we have collected and to explain the competitive landscape in that sector. Next, building on the quantitative insights obtained in Study 1, Study 2 examines branded social media posts for these three brands and highlights the qualitative differences in tone, focus, and content that appear in posts that occurred during the observation period. Lastly, we conclude by briefly discussing the analytical approaches that were used for this research and considering the ways that marketers can use multimethod research techniques to acquire richer insights about their customers and their competitors