Social informatics

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

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

EnTagRec(++): An enhanced tag recommendation system for software information sites

Software engineers share experiences with modern technologies using software information sites, such as Stack Overflow. These sites allow developers to label posted content, referred to as software objects, with short descriptions, known as tags. Tags help to improve the organization of questions and simplify the browsing of questions for users. However, tags assigned to objects tend to be noisy and some objects are not well tagged. For instance, 14.7% of the questions that were posted in 2015 on Stack Overflow needed tag re-editing after the initial assignment. To improve the quality of tags in software information sites, we propose EnTagRec++, which is an advanced version of our prior work EnTagRec. Different from EnTagRec, EnTagRec++ does not only integrate the historical tag assignments to software objects, but also leverages the information of users, and an initial set of tags that a user may provide for tag recommendation. We evaluate its performance on five software information sites, Stack Overflow, Ask Ubuntu, Ask Different, Super User, and Freecode. We observe that even without considering an initial set of tags that a user provides, it achieves Recall@5 scores of 0.821, 0.822, 0.891, 0.818 and 0.651, and Recall@10 scores of 0.873, 0.886, 0.956, 0.887 and 0.761, on Stack Overflow, Ask Ubuntu, Ask Different, Super User, and Freecode, respectively. In terms of Recall@5 and Recall@10, averaging across the 5 datasets, it improves upon TagCombine, which is the prior state-of-the-art approach, by 29.3% and 14.5% respectively. Moreover, the performance of our approach is further boosted if users provide some initial tags that our approach can leverage to infer additional tags: when an initial set of tags is given, Recall@5 is improved by 10%

Learning Topical Social Media Sensors for Twitter

Social media sources such as Twitter represent a massively distributed social sensor over diverse topics ranging from social and political events to entertainment and sports news. However, due to the overwhelming volume of content, it can be difficult to identify novel and significant content within a broad topic in a timely fashion. To this end, this thesis proposes a scalable and practical method to automatically construct social sensors for generic topics. The concept of using social media as a sensor for detection of events and news has been proposed in the literature. However, we argue that most of these works do not focus on targeted content detection or they use very basic methods for collecting the topical data for further analysis. This demonstrates a gap in the use of social media as a sensor for high-quality topical content detection that we aim to address via machine learning. In this thesis, given minimal supervised training content from a user, we learn to identify topical tweets from millions of features capturing content, user and social interactions on Twitter. On a corpus of over 800 million English Tweets collected from the Twitter streaming API during 2013 and 2014 and learning for 10 diverse topics, we empirically show that our learned social sensor automatically generalizes to unseen future content with high ranking and precision scores. Furthermore, we provide an extensive analysis of features and feature types across different topics that reveals, for example, that (1) largely independent of topic, simple terms are the most informative feature followed by location features and that (2) the number of unique hashtags and tweets by a user correlates more with their informativeness than their follower or friend count. In summary, this work provides a novel, effective, and efficient way to learn topical social sensors requiring minimal user curation effort and offering strong generalization performance for identifying future topical content

Political Science and Digitalization – Global Perspectives

Digitalization is not only a new research subject for political science, but a transformative force for the discipline in terms of teaching and learning as well as research methods and publishing. This volume provides the first account of the influence of digitalization on the discipline of political science including contributions from 20 different countries. It presents a regional stocktaking of the challenges and opportunities of digitalization in most world regions

Political Science and Digitalization – Global Perspectives

Digitalization is not only a new research subject for political science, but a transformative force for the discipline in terms of teaching and learning as well as research methods and publishing. This volume provides the first account of the influence of digitalization on the discipline of political science including contributions from 20 different countries. It presents a regional stocktaking of the challenges and opportunities of digitalization in most world regions

Political Science and Digitalization – Global Perspectives

Digitalization is not only a new research subject for political science, but a transformative force for the discipline in terms of teaching and learning as well as research methods and publishing. This volume provides the first account of the influence of digitalization on the discipline of political science including contributions from 20 different countries. It presents a regional stocktaking of the challenges and opportunities of digitalization in most world regions