Learning Linguistic Descriptors of User Roles in Online Communities

Abstract

Understanding the ways in which users interact with different online communities is crucial to social network analysis and community maintenance. We present an unsupervised neural model to learn linguistic descriptors for a user's behavior over time within an online community. We show that the descriptors learned by our model capture the functional roles that users occupy in communities, in contrast to those learned via a standard topic-modeling algorithm, which simply reflect topical content. Experiments on the social media forum Reddit show how the model can provide interpretable insights into user behavior. Our model uncovers linguistic differences that correlate with user activity levels and community clustering.

Ten Social Dimensions of Conversations and Relationships

“…Past research in Web Mining and Natural Language Processing (NLP) studied aspects pertaining some of the dimensions we deal with in this work [34,78], with special attention to concepts at the extremes of the spectrum of sentiment such as conflict [70] or empathy [86,92] and support [109,114]. The operationalization of some of these concepts proved useful to improve the accuracy of prediction tasks [17,84,108,111].…”

No Permanent

“…Most relevant to our work is Iyyer et al (2016), which suggests RMN better capture dynamic relationships in literature than hidden Markov model (Gruber et al, 2007) and LDA (Blei et al, 2003). Recent work extended and applied RMN to other settings such as studying user roles in online communities (Wang et al, 2016;Frermann and Szarvas, 2017). Notably, Chaturvedi et al (2017) suggests HMM with shallow linguistic features (i.e., frame net parses) and global constraints can outperform RMN for modeling relations in literature.…”

Análise de Comunidades de Suporte a Transtornos de Saúde Mental do Reddit

“…Esses mesmos autores fizeram outro trabalho [Gkotsis et al 2017] analisando as postagens do Reddit para desenvolver classificadores que reconheçam e classifiquem postagens relacionadasà doença mental, através da técnica de deep learning. Além disso, existem trabalhos [Souza et al 2017, Wang et al 2016 que abordam a caracterização de informações, através de extração de tópicos a fim de descrever o contexto analisado.…”

“…Primeiramente, foram removidos todos os posts e comentários marcados como [deleted] ou [removed], as stopwords (aplicando a biblioteca NLTK) e as pontuações. Segundo, foram considerados somente os posts e comentários dos usuários que realizaram no mínimo 50 atividades (posts/comentários) em cada subreddit, seguindo a metodologia apresentada em [Wang et al 2016]. Por fim, foram selecionadas todas as palavras que aparecem nos quatro subreddits analisados, buscando encontrar semelhanças na forma com que as pessoas se expressam quando discutem sobre transtornos de saúde mental.…”