[Paper Review] Using Sentiment Induction to Understand Variation in Gendered Online Communities

Li Lucy, Julia Mendelsohn|arXiv (Cornell University)|Nov 16, 2018

Authorship Attribution and Profiling32 references4 citations

TL;DR

This paper uses sentiment induction to analyze gendered online communities on Reddit, revealing that sentiment-based representations uncover nuanced social meanings beyond word choice. It shows that words like 'ladies' and 'feminists' carry distinct affective meanings across communities, demonstrating sentiment as a key dimension for understanding identity construction in online spaces.

ABSTRACT

We analyze gendered communities defined in three different ways: text, users, and sentiment. Differences across these representations reveal facets of communities' distinctive identities, such as social group, topic, and attitudes. Two communities may have high text similarity but not user similarity or vice versa, and word usage also does not vary according to a clearcut, binary perspective of gender. Community-specific sentiment lexicons demonstrate that sentiment can be a useful indicator of words' social meaning and community values, especially in the context of discussion content and user demographics. Our results show that social platforms such as Reddit are active settings for different constructions of gender.

Motivation & Objective

• To understand how sentiment in online communities reflects social identities beyond simple gender binaries.
• To investigate how sentiment-based representations reveal community-specific meanings of words that are denotationally similar.
• To examine the intersection of user demographics, content, and affective meaning in gendered subreddits.
• To challenge the assumption that sentiment alone captures social meaning, especially in contexts involving sarcasm or patronizing language.
• To demonstrate that sentiment is a salient semantic dimension for uncovering sociolinguistic variation in online communities.

Proposed method

• Constructed sentiment lexicons specific to individual subreddits using a fine-tuned version of the SentProp model.
• Trained sentiment classifiers on subreddit-specific data to generate sentiment scores for words across communities.
• Used bootstrap resampling (50 runs) to estimate standard errors and confidence intervals for sentiment scores.
• Compared sentiment scores of key gendered terms (e.g., 'women', 'ladies', 'feminists') across multiple subreddits to detect variation.
• Combined quantitative sentiment analysis with qualitative interpretation of community discourse to contextualize sentiment findings.
• Analyzed user-based clusters and text-based representations to compare sentiment with user overlap and lexical similarity.

Experimental results

Research questions

• RQ1 How do sentiment scores of gendered terms like 'ladies' and 'feminists' vary across different gendered subreddits?
• RQ2 To what extent do sentiment-based representations reveal sociolinguistic variation that is not captured by word choice or user membership alone?
• RQ3 How do community norms and discourse practices affect the sentiment of words that are denotationally similar?
• RQ4 In what ways does sentiment reflect community identity, especially in contexts involving irony, patronizing language, or in-group labeling?
• RQ5 Can sentiment-based models detect differences in social meaning even when lexical or user-based similarity is high?

Key findings

• The word 'feminists' carries a highly negative sentiment in r/mensrights and r/actuallesbians, though for different reasons—opposition to perceived exclusivity in LGBTQ+ spaces.
• The term 'ladies' has a positive sentiment in r/actuallesbians, where it functions as an in-group identifier, but is used patronizingly in r/mensrights, despite similar sentiment scores.
• SentProp-based sentiment scores for 'omg' are highly positive in women-oriented communities like r/femalefashionadvice, indicating affective engagement, but are used more neutrally or ironically in men-oriented subreddits.
• High text similarity between subreddits does not imply high user overlap or sentiment similarity, indicating that sentiment captures distinct social dimensions.
• Sentiment-based representations reveal that words like 'women' and 'ladies' carry distinct connotative meanings across communities, even when their denotations are similar.
• The standard deviation in sentiment scores across bootstrap runs highlights inconsistencies in sentiment models, especially in contexts involving sarcasm or irony, suggesting limitations of purely vector-based sentiment analysis.