Online communities provide a unique way for individuals to access information from those in similar circumstances, which can be critical for health conditions that require daily and personalized management. As these groups and topics often arise organically, identifying the types of topics discussed is necessary to understand their needs. As well, these communities and people in them can be quite diverse, and existing community detection methods have not been extended towards evaluating these heterogeneities. This has been limited as community detection methodologies have not focused on community detection based on semantic relations between textual features of the user-generated content. Thus here we develop an approach, NeuroCom, that optimally finds dense groups of users as communities in a latent space inferred by neural representation of published contents of users. By embedding of words and messages, we show that NeuroCom demonstrates improved clustering and identifies more nuanced discussion topics in contrast to other common unsupervised learning approaches.
Neural representation has been implemented for social media short messages albeit in different ways than we propose @cite_1 . Gaussian Restricted Boltzmann Machines (RBM) have been used for modeling user's posts within a social network to identify their topics of interest, and finally construct communities @cite_1 . However, a parametric approach was used in which it was necessary to specify the number of clusters communities. In addition to not requiring such initialization, this approach is conceptually different than our proposed work in that it directly maps individuals to communities (instead of mapping the content of their posts, which may better capture heterogeneous community memberships). Further, content-based density approachefs as proposed, versus parametric ones could potentially learn a more organic number of communities. Given this gap, and the fact that content-based community detection (opposed to graph-based) may be more pertinent in health-related communities, here we explore content-based clustering of health communities.
@cite_1
2078525157
Online social networks have been wildly spread in recent years. They enable users to identify other users with common interests, exchange their opinions, and expertise. Discovering user communities from social networks have become one of the major challenges which help its members to interact with relevant people who have similar interests. Community detection approaches fall into two categories: the first one considers user' networks while the other utilizes usergenerated content. In this paper, a multi-layer community detection model based on identifying topics of interest from user published content is presented. This model applies Gaussian Restricted Boltzmann Machine for modeling user's posts within a social network which yields to identify their topics of interest, and finally construct communities. The effectiveness of the proposed multi-layer model is measured using KL divergence which measures similarity between users of the same community. Experiments on the real Twitter dataset show that the proposed deep model outperforms traditional community detection models that directly maps users into corresponding communities using several baseline techniques. Self-disclosure is an important element facilitating improved psychological wellbeing in individuals with mental illness. As social media is increasingly adopted in health related discourse, we examine how these new platforms might be allowing honest and candid expression of thoughts, experiences and beliefs. Specifically, we seek to detect levels of self-disclosure manifested in posts shared on different mental health forums on Reddit. We develop a classifier for the purpose based on content features. The classifier is able to characterize a Reddit post to be of high, low, or no self-disclosure with 78 accuracy. Applying this classifier to general mental health discourse on Reddit, we find that bulk of such discourse is characterized by high self-disclosure, and that the community responds distinctively to posts that disclose less or more. We conclude with the potential of harnessing our proposed self-disclosure detection algorithm in psychological therapy via social media. We also discuss design considerations for improved community moderation and support in these vulnerable self-disclosing communities. Recently community detection has attracted much interest in social media to understand the collective behaviours of users and allow individuals to be modeled in the context of the group. Most existing approaches for community detection exploit either users' social links or their published content, aiming at discovering groups of densely connected or highly similar users. They often fail to find effective communities due to excessive noise in content, sparsity in links, and heterogenous behaviours of users in social media. Further, they are unable to provide insights and rationales behind the formation of the group and the collective behaviours of the users. To tackle these challenges, we propose to discover communities in a low- dimensional latent space in which we simultaneously learn the representation of users and communities. In particular, we integrated different social views of the network into a low-dimensional latent space in which we sought dense clusters of users as communities. By imposing a Laplacian regularizer into affiliation matrix, we further incorporated prior knowledge into the community discovery process. Finally community profiles were computed by a linear operator integrating the profiles of members. Taking the wellness domain as an example, we conducted experiments on a large scale real world dataset of users tweeting about diabetes and its related concepts, which demonstrate the effectiveness of our approach in discovering and profiling user communities. Creating, placing, and presenting social media content is a difficult problem. In addition to the quality of the content itself, several factors such as the way the content is presented (the title), the community it is posted to, whether it has been seen before, and the time it is posted determine its success. There are also interesting between these factors. For example, the language of the title should be targeted to the community where the content is submitted, yet it should also highlight the distinctive nature of the content. In this paper, we examine how these factors interact to determine the popularity of social media content. We do so by studying resubmissions, i.e., content that has been submitted multiple times, with multiple titles, to multiple different communities. Such data allows us to tease apart' the extent to which each factor influences the success of that content. The models we develop help us understand how to better target social media content: by using the right title, for the right community, at the right time.