Ten Social Dimensions of Conversations and Relationships

Abstract

Decades of social science research identified ten fundamental dimensions that provide the conceptual building blocks to describe the nature of human relationships. Yet, it is not clear to what extent these concepts are expressed in everyday language and what role they have in shaping observable dynamics of social interactions. After annotating conversational text through crowdsourcing, we trained NLP tools to detect the presence of these types of interaction from conversations, and applied them to 160M messages written by geo-referenced Reddit users, 290k emails from the Enron corpus and 300k lines of dialogue from movie scripts. We show that social dimensions can be predicted purely from conversations with an AUC up to 0.98, and that the combination of the predicted dimensions suggests both the types of relationships people entertain (conflict vs. support) and the types of real-world communities (wealthy vs. deprived) they shape.

Cognitive Network Science Reconstructs How Experts, News Outlets and Social Media Perceived the COVID-19 Pandemic

“…The current methodology can also identify how people perceive health risks and concrete symptoms of COVID-19, integrating recent powerful analyses extracting the symptoms of the novel coronavirus from social discourse [35] and complementing interesting dynamical patterns of grief elaboration and COVID-19 recently unearthed in social platforms by Aiello and colleagues [15]. Notice that the representation of textual knowledge in tweets produced here could also be integrated with other representations based on word embedding models [32,34,36] and powering recent natural language approaches to identifying topics in tweets discussing COVID-19 [36,37]. Forma mentis networks and other models of natural language processing all aim towards the common direction of monitoring and understanding large volumes of messages with the ease of lightweight and automatic knowledge extraction methodologies.…”

“…Future research should build upon cognitive network science for a better understanding of social media, possibly in synergy with other promising and successful automatic approaches to knowledge modelling [32][33][34][35][36][37]. Access to large-scale corpora of news media articles would enable prompt identification of outlets promoting distorted mindsets ("COVID-19 is just a flu") or panic-inducing misinformation.…”

Socio-semantic networks as mutualistic networks

“…In future developments, our exploration could be replicated with different types of ties. Although observed relationships online are limited to posts, tweets or email exchanges, it might be possible to infer the type of relationships from the text itself in a way similar as that proposed by Choi et al 50 who tried to find the presence of the 10 types of relationship proposed by Deri et al 51 typology in various corpora, including the ENRON dataset. With similar machine learning approaches, it might be possible to infer types of ties based on the various typologies of social relationships that have been studied in social network analysis 52 – 54 .…”

Conversations Gone Alright: Quantifying and Predicting Prosocial Outcomes in Online Conversations

“…A few recent approaches examining specific factors related to positive conversational outcomes like constructive comments [59,60], politeness [24], supportiveness [78], or empathy [15,70,86]; or, showing that, in general, online prosocial behaviors mirror offline trends [82]. In the majority of cases, only individual dimensions have been analyzed; however, we note that recent work has proposed studying these dimensions jointly in relationships and social interactions [22] using the ten social dimensions outlined in Deri et al [25]. Similar to the present work, Choi et al [22] examines general factors from sociological and psychological literature for relationships to study interactions; however, the factors used here are specifically grounded in prosocial literature and include behavioral factors in addition to linguistic factors.…”

“…In the majority of cases, only individual dimensions have been analyzed; however, we note that recent work has proposed studying these dimensions jointly in relationships and social interactions [22] using the ten social dimensions outlined in Deri et al [25]. Similar to the present work, Choi et al [22] examines general factors from sociological and psychological literature for relationships to study interactions; however, the factors used here are specifically grounded in prosocial literature and include behavioral factors in addition to linguistic factors. A few studies have tried to measure prosocial behavior as a single variable [32,33]; however, these approaches in practice have used lexicons that recognize only a subset of the possible prosocial behaviors focused on collective interest and interpersonal harmony.…”