Social media provides valuable insights into societal opinions and user interactions. Social landscapes, created from these interactions, offer a comprehensive view of online conversations and social media dynamics. The development of advanced data analytics tools has made the creation of social landscapes for larger populations increasingly common and accessible for researchers. This underscores the importance of clearly defining and organizing the insights we seek from social landscapes. We introduce a methodology to analyze the social structure through social landscapes. We have identified 12 key elements that encapsulate the insights expected from social landscapes. Then, we integrate two comprehensive social landscape approaches into our methodology that effectively provide insights into the key elements previously outlined. These two approaches illuminate different facets of social media dynamics: one focuses on the content generated and the other on relationship-based interactions. First, we revisit the concept ofgalaxiesas a single time-based snapshot of large-scale online conversations. Second, we introduce a technique that utilizes the network of user interactions on social media to map social structures. We propose a novel method to construct a sociopolitical spectrum using discourse trajectory inference (pseudotime transformation), marking its first use outside bioinformatics literature. Finally, we take the introduced methodology to evaluate how each social landscape enhances our understanding of social media dynamics. These insights can help to structure the insights we aim to extract from social landscapes and provide practical tools for media analysts and strategists aiming to analyze social media dynamics effectively.

The emergence of social media has facilitated new ways of communication and social engagement. Political elites and public figures use social media to inform and mobilize users. Discussions appearing around these tweets reflect valuable information from the communities, their interests, and concerns. The majority of the studies on conversational dynamics on social media use the network between users to track the flow of information and impact. In this study, a different approach is introduced in which discussion threads as the network between tweets are considered as the main components of the analysis. Based on this approach, a novel framework is proposed to provide a high-level overview of a Twitter stream. The metric energy is introduced to quantify user engagement in discussions initiated by each tweet and to study the patterns of user-participation overtime. To mine the content of the tweets, some recently developed text analysis methods are integrated into our model to extract the topic of the tweets, the sentiment of the tweets, the stance of tweets toward a controversial topic, and the coherency between the tweet-reply pairs. Three different data sets from politics, healthcare, and sport are collected to be used as case studies. The proposed framework provides a better understanding of how opinions form around a tweet, how different topics to bond together and make the whole discussions, for which topics discussions are more coherent, and which users are able to initiate a cross cutting and coherent conversation over social media.

Performance issues compromise the response time and resource consumption of a software system. Modern software systems use issue tracking systems to manage all kinds of issue reports, including performance issues. The problem is that performance issues are often not explicitly tagged. The tagging mechanism, if exists, is completely voluntary, depending on the project’s convention and on submitters’ discipline. For example, the performance tag rate in Apache’s Jira system is below 1%. This paper contributes a hybrid classification approach that combines linguistic patterns and machine/deep learning techniques to automatically detect performance issue reports. We manually analyzed 980 real-life performance issue reports and derived 80 project-agnostic linguistic patterns that recur in the reports. Our approach uses these linguistic patterns to construct the sentence-level and issue-level learning features for training effective machine/deep learning classifiers. We test our approach on two separate datasets, each consisting of 980 unclassified issue reports, and compare the results with 31 baseline methods. Our approach can reach up to 83% precision and up to 59% recall. The only comparable baseline method is BERT, which is still 25% lower in the F1-score.