Digital Mixed Content Analysis on Digital Platform Social Data

The explosion of platform social data as digital secondary data, collectible through sophisticated and automatized query systems or algorithms, makes it possible to accumulate huge amounts of dense and miscellaneous data. The challenge for social researchers becomes how to extract meaning and not only trends in a quantitative as well as in a qualitative manner. Through the application of a digital mixed content analysis perspective to data analysis, in this contribution, the author will present the potentiality of a hybrid digitalized approach to social content. This perspective should be seen as an applied example of organizing a framework to guide the application of integrated methods of content analysis (quantitative and qualitative) but also integrated objects of analysis (individuals, relationships, and digital actions) on digital platform social data and to address their varied nature.

Temporal Geo-Social Personalized Keyword Search Over Streaming Data

The unprecedented rise of social media platforms, combined with location-aware technologies, has led to continuously producing a significant amount of geo-social data that flows as a user-generated data stream. This data has been exploited in several important use cases in various application domains. This article supports geo-social personalized queries in streaming data environments. We define temporal geo-social queries that provide users with real-time personalized answers based on their social graph. The new queries allow incorporating keyword search to get personalized results that are relevant to certain topics. To efficiently support these queries, we propose an indexing framework that provides lightweight and effective real-time indexing to digest geo-social data in real time. The framework distinguishes highly dynamic data from relatively stable data and uses appropriate data structures and a storage tier for each. Based on this framework, we propose a novel geo-social index and adopt two baseline indexes to support the addressed queries. The query processor then employs different types of pruning to efficiently access the index content and provide a real-time query response. The extensive experimental evaluation based on real datasets has shown the superiority of our proposed techniques to index real-time data and provide low-latency queries compared to existing competitors.

BISoN: A Bayesian Framework for Inference of Social Networks

Social networks are often constructed from point estimates of edge weights. In many contexts, edge weights are inferred from observational data, and the uncertainty around point estimates can be affected by various factors. Though this has been acknowledged in previous work, methods that explicitly quantify uncertainty in edge weights have not yet been widely adopted, and remain undeveloped for common types of data. Furthermore, existing methods are unable to cope with some of the complexities often found in observational data, and do not propagate uncertainty in edge weights to subsequent analyses. We introduce a unified Bayesian framework for modelling social networks based on observational data. This framework, which we call BISoN, can accommodate many common types of observational social data, can capture confounds and model effects at the level of observations, and is fully compatible with popular methods of social network analysis. We show how the framework can be applied to common types of data and how various types of downstream analyses can be performed, including non-random association tests and regressions on network properties. Our framework opens up the opportunity to test new types of hypotheses, make full use of observational datasets, and increase the reliability of scientific inferences. We have made example R code available to enable adoption of the framework.

Contrapuntal Connectedness: Analysing Relations Between Social Media Data and Ethnography in Digital Migration Studies

This chapter presents a rethinking of the relationship between ethnography and so-called big social data as being comparable to those between a sum and its parts (Strathern 1991/2004). Taking cue from Tim Ingold’s one world anthropology (2018) the chapter argues that relations between ethnography and social media data can be established as contrapuntal. That is, the types of material are understood as different, yet fundamentally interconnected. The chapter explores and qualifies this affinity with the aim of identifying potentials and further questions for digital migration research. The chapter is based on ethnographic fieldwork carried out with Syrian refugees and solidarians in the Danish–Swedish borderlands in 2018–2019 as well as data collected for 2011–2018 from 200 public Facebook pages run by solidarity organisations, NGOs, and informal refugee welcome and solidarity groups.

Twitter Sentiment Analysis on Government Law Using Real Time Data

Social media monitoring has been growing day by day so analyzing social data plays an important role in knowing people's behavior. So we are analyzing Social data such as Twitter Tweets using sentiment analysis which checks the opinion of people related to government schemes that are announced by the Central Government. This paper-based is on social media Twitter datasets of particular schemes and their polarity of sentiments. The popularity of the Internet has been rapidly increased. Sentiment analysis and opinion mining is the field of study that analyses people's opinions, sentiments, evaluations, attitudes, and emotions from written language. User-generated content is highly generated by users. The growing importance of sentiment analysis coincides with the growth of social media such as reviews, forum discussions, blogs, micro-blogs, Twitter, and social networks. It is difficult to analyze or summarize user-generated content. Most of the users write their opinions, thoughts on blogs, social media sites, E-commerce sites, etc. So these contents are very important for individuals, industry, government, and research work to make decisions. This Sentiment analysis and opinion mining research is a hot research area that comes under Natural Language processing. We plot and calculate numbers of positive, negative, and neutral tweets from each event.