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This paper aims that privacy research is divided in distinct communities and rarely considered as a singular field, harming its disciplinary identity. The authors collected 119.810 publications and over 3 million references to perform a bibliometric domain analysis as a quantitative approach to uncover the structures within the privacy research field.

The bibliometric domain analysis consists of a combined directed network and topic model of published privacy research. The network contains 83,159 publications and 462,633 internal references. A Latent Dirichlet allocation (LDA) topic model from the same dataset offers an additional lens on structure by classifying each publication on 36 topics with the network data. The combined outcomes of these methods are used to investigate the structural position and topical make-up of the privacy research communities.

The authors identified the research communities as well as categorised their structural positioning. Four communities form the core of privacy research: individual privacy and law, cloud computing, location data and privacy-preserving data publishing. The latter is a macro-community of data mining, anonymity metrics and differential privacy. Surrounding the core are applied communities. Further removed are communities with little influence, most notably the medical communities that make up 14.4% of the network. The topic model shows system design as a potentially latent community. Noteworthy is the absence of a centralised body of knowledge on organisational privacy management.

This is the first in-depth, quantitative mapping study of all privacy research.

Most of the existing literature on online social networks (OSNs) either focuses on community detection in graphs without considering the topic of the messages exchanged, or concentrates exclusively on the messages without taking into account the social links. The purpose of this paper is to characterise the semantic cohesion of such groups through the introduction of new measures.

A theoretical model for social links and salient topics on Twitter is proposed. Also, measures to evaluate the topical cohesiveness of a group are introduced. Inspired from precision and recall, the proposed measures, called expertise and representativeness, assess how a set of groups match the topic distribution. An adapted measure is also introduced when a topic similarity can be computed. Finally, a topic relevance measure is defined, similar to tf.idf (term-frequency, inverse document frequency).

The measures yield interesting results, notably on a large tweet corpus: the metrics accurately describe the topics discussed in the tweets and enable to identify topic-focused groups. Combined with topological measures, they provide a global and concise view of the detected groups.

Many algorithms, applied on OSN, detect communities which often lack of meaning and internal semantic cohesion. This paper is among the first to quantify this aspect, and more precisely the topical cohesion and topical relevance of a group. Moreover, the proposed indicators can be exploited for social media monitoring, to investigate the impact of a group of people: for instance, they could be used for journalism, marketing and security purposes.

This chapter investigates the behavior of Reddit’s news subreddit users and the relationship between their sentiment on exchange rates. Using graphical models and natural language processing, hidden online communities among Reddit users are discovered. The data set used in this project is a mixture of text and categorical data from Reddit’s news subreddit. These data include the titles of the news pages, as well as a few user characteristics, in addition to users’ comments. This data set is an excellent resource to study user reaction to news since their comments are directly linked to the webpage contents. The model considered in this chapter is a hierarchical mixture model which is a generative model that detects overlapping networks using the sentiment from the user generated content. The advantage of this model is that the communities (or groups) are assumed to follow a Chinese restaurant process, and therefore it can automatically detect and cluster the communities. The hidden variables and the hyperparameters for this model are obtained using Gibbs sampling.

The paper addresses the problem of what drives the formation of latent discussion communities, if any, in the blogosphere: topical composition of posts or their authorship? The purpose of this paper is to contribute to the knowledge about structure of co-commenting.

The research is based on a dataset of 17,386 full text posts written by top 2,000 LiveJournal bloggers and over 520,000 comments that result in about 4.5 million edges in the network of co-commenting, where posts are vertices. The Louvain algorithm is used to detect communities of co-commenting. Cosine similarity and topic modeling based on latent Dirichlet allocation are applied to study topical coherence within these communities.

Bloggers unite into moderately manifest communities by commenting roughly the same sets of posts. The graph of co-commenting is sparse and connected by a minority of active non-top commenters. Communities are centered mainly around blog authors as opinion leaders and, to a lesser extent, around a shared topic or topics.

The research has to be replicated on other datasets with more thorough hand coding to ensure the reliability of results and to reveal average proportions of topic-centered communities.

Knowledge about factors around which co-commenting communities emerge, in particular clustered opinion leaders that often attract such communities, can be used by policy makers in marketing and/or political campaigning when individual leadership is not enough or not applicable.

The research contributes to the social studies of online communities. It is the first study of communities based on co-commenting that combines examination of the content of commented posts and their topics.

Software developers extensively use stack overflow (SO) for knowledge sharing on software development. Thus, software engineering researchers have started mining the structured/unstructured data present in certain software repositories including the Q&A software developer community SO, with the aim to improve software development. The purpose of this paper is show that how academics/practitioners can get benefit from the valuable user-generated content shared on various online social networks, specifically from Q&A community SO for software development.

A comprehensive literature review was conducted and 166 research papers on SO were categorized about software development from the inception of SO till June 2016.

Most of the studies revolve around a limited number of software development tasks; approximately 70 percent of the papers used millions of posts data, applied basic machine learning methods, and conducted investigations semi-automatically and quantitative studies. Thus, future research should focus on the overcoming existing identified challenges and gaps.

The work on SO is classified into two main categories; “SO design and usage” and “SO content applications.” These categories not only give insights to Q&A forum providers about the shortcomings in design and usage of such forums but also provide ways to overcome them in future. It also enables software developers to exploit such forums for the identified under-utilized tasks of software development.

The study is the first of its kind to explore the work on SO about software development and makes an original contribution by presenting a comprehensive review, design/usage shortcomings of Q&A sites, and future research challenges.

The identification of network user relationship in Fancircle contributes to quantifying the violence index of user text, mining the internal correlation of network behaviors among users, which provides necessary data support for the construction of knowledge graph.

A correlation identification method based on sentiment analysis (CRDM-SA) is put forward by extracting user semantic information, as well as introducing violent sentiment membership. To be specific, the topic of the implementation of topology mapping in the community can be obtained based on self-built field of violent sentiment dictionary (VSD) by extracting user text information. Afterward, the violence index of the user text is calculated to quantify the fuzzy sentiment representation between the user and the topic. Finally, the multi-granularity violence association rules mining of user text is realized by constructing violence fuzzy concept lattice.

It is helpful to reveal the internal relationship of online violence under complex network environment. In that case, the sentiment dependence of users can be characterized from a granular perspective.

The membership degree of violent sentiment into user relationship recognition in Fancircle community is introduced, and a text sentiment association recognition method based on VSD is proposed. By calculating the value of violent sentiment in the user text, the annotation of violent sentiment in the topic dimension of the text is achieved, and the partial order relation between fuzzy concepts of violence under the effective confidence threshold is utilized to obtain the association relation.

This paper aims to investigate user health information seeking and sharing patterns and content in an online diabetes community and explore the similarities and differences in the ways and themes they expressed.

Multiple methods are applied to analyze the expressions and themes that users seek and share based on large-scale text data in an online diabetes community. First, a text classifier using deep learning method is performed based on the expression category this study developed. Second, statistical and social network analyses are used to measure the popularity and compare differences between expressions. Third, topic modeling, manual coding and similarity analysis are used to mining topics and thematic similarity between seeking and sharing threads.

There are four different ways users seek and share in online health communities (OHCs) including informational seeking, situational seeking, objective information sharing and experiential information sharing. The results indicate that threads with self-disclosure could receive more replies and attract more users to contribute. This study also examines the 10 topics that were discussed for information seeking and 14 topics for information sharing. They shared three discussion themes: self-management, medication and symptoms. Information about symptoms can be largely matched between seeking and sharing threads while there is less overlap in self-management and medication categories.

Being different from previous studies that mainly describe one type of health information behavior, this paper analyzes user health information seeking and sharing behaviors in OHCs and investigates whether there is a correspondence or discrepancy between expressions and information users spontaneously seek and share in OHCs.

This study aims to explore the evolutionary pattern of discussion topics over time in an online depression self-help community.

Using the Latent Dirichlet Allocation (LDA) method, the authors analyzed 17,534 posts and 138,567 comments posted over 8 years on an online depression self-help group in China and identified the major discussion topics. Based on significant changes in the frequency of posts over time, the authors identified five stages of development. Through a comparative analysis of discussion topics in the five stages, the authors identified the changes in the extent and range of topics over time. The authors discuss the influence of socio-cultural factors on depressed individuals' health information behavior.

The results illustrate an evolutionary pattern of topics in users' discussion in the online depression self-help group, including five distinct stages with a sequence of topic changes. The discussion topics of the group included self-reflection, daily record, peer diagnosis, companionship support and instrumental support. While some prominent topics were discussed frequently in each stage, some topics were short-lived.

While most prior research has ignored topic changes over time, the study takes an evolutionary perspective of online discussion topics among depressed individuals. The authors provide a nuanced account of the progression of topics through five distinct stages, showing that the community experienced a sequence of changes as it developed. Identifying this evolutionary pattern extends the scope of research on depression therapy in China and offers a deeper understanding of the support that individuals with depression seek, receive and provide online.

The purpose of this paper is to examine how entrepreneurs engage in a virtual community of practice (VCoP) to share knowledge. Intensity of engagement is taken as a proxy to measure the strength of knowledge sharing.

The archival data spanning over a three-year period from “Start-up-Nation^©” (a VCoP purposefully setup for entrepreneurs) are used for analysis. A set of indices are introduced to measure participants’ intensity of engagement in terms of message length, message frequency and reciprocity in the knowledge sharing process. Content analysis is employed to test a sample of “highly engaged”, “moderately engaged”, “low engaged” and “not engaged” discussion topics as part of the online discourse.

The authors find that entrepreneurs normally use short (fewer than 100 words) or medium (fewer than 250 words) message size to contribute to the discussions. In addition, the authors find that senior members and discussion moderators play important roles in igniting the “reciprocity” behaviour in stimulating the interest of the community with the topic discussion. The authors also find that highly engaged topics usually lead to further discussion threads.

This is the first study of its kind to explore how entrepreneurs engage in a VCoP to share their knowledge and experiences. The set of measurement indices tested here provide a tool for the owner, designer and moderator of the VCoP to measure the utility of their website in terms of its members’ participation. In addition, the set of textual and subjective interventions identified here enables the moderator (administrator) of a VCoP to design effective interventions to facilitate online discourse and augments the knowledge sharing process amongst its community members.

The purpose of this paper is to present an algorithm for detecting communities in social networks.

The majority of existing methods of community detection in social networks are based on structural information, and they neglect the content information. In this paper, the authors propose a novel approach that combines the content and structure information to discover more meaningful communities in social networks. To integrate the content information in the process of community detection, the authors propose to exploit the texts involved in social networks to identify the users’ topics of interest. These topics are detected based on the statistical and semantic measures, which allow us to divide the users into different groups so that each group represents a distinct topic. Then, the authors perform links analysis in each group to discover the users who are highly interconnected (communities).

To validate the performance of the approach, the authors carried out a set of experiments on four real life data sets, and they compared their method with classical methods that ignore the content information.

The experimental results demonstrate that the quality of community structure is improved when we take into account the content and structure information during the procedure of community detection.