I've been playing around with some AI tools lately and worked with one to do a sentiment analysis to see how men and women were treated differently on online forums. Reddit is thought to be skewed more toward males, so I'm guessing the bias is even greater elsewhere. Results are below.

Here was the prompt:
I'd like to do a small research activity. I'd like to do some searches, limiting the results to forum responses. The searches will be in pairs with the only difference between each search in the pair being the replacement of either "boyfriend" with "girlfriend" (or vice versa), or the replacement of "husband" with "wife", or vice versa. The purpose of this project is to highlight the differences in response sentiment by gender. For example, my hypothesis is that when I search for "My boyfriend messages other girls on social media" the sentiment will be negative toward the boyfriend, and will express that the girlfriend should leave him, or that he's cheating. However, if I search for "my girlfriend messages other guys on social media" the responses will still be negative toward the boyfriend, who is asking the question in this case. They will say things like he's being too jealous or insecure. Please help me prove or disprove this hypothesis with data from Reddit responses.

There was a long back and forth about how to go about doing this with web scraping, Python scripts, the Reddit API, etcetera, but here are the results, starting with the summary and going into deeper data.

Key Sentiment Differences

1. Messaging Others:

• Male behavior receives 1,367% more negative sentiment than female behavior
• You read that right. 1,367% more negative toward men.
• Boyfriend sentiment: -0.128 (negative)
• Girlfriend sentiment: -0.009 (slightly negative)
• This suggests a significant bias in how similar behaviors are judged

1. Working Late:

• Female behavior receives 33% more positive sentiment
• Husband sentiment: +0.086 (slightly positive)
• Wife sentiment: +0.128 (positive)
• However, husband-related posts get 5-6x more engagement

Key Findings

Gender Bias in Judgment:

1. Similar behaviors receive significantly different sentiment scores based on gender
2. Male infidelity concerns receive more negative sentiment
3. Female working late receives more positive sentiment but less engagement
4. Phone privacy issues get the highest engagement
5. Posts about male behavior generally receive more upvotes and comments
6. Working late scenarios get less engagement overall
7. Male-focused posts tend to have more direct accusatory language
8. Female-focused posts often include more context and justification
9. Comments about male behavior are more likely to suggest immediate action (breaking up, confrontation)

------
End of results, start of personal opinion

------

I see this all the time. I knew it was an issue but wanted to make sure it wasn't just confirmation bias. It isn't. When a man cheats, he's a piece of $hit. It's 100% his fault and she should leave him immediately and take all of his money. When a woman cheats, it's ALSO the man's fault for not "meeting her needs" or "being emotionally available" or whatever. When a boyfriend texts a female friend, he's "probably cheating" and she should "check his phone" without his permission. When a girlfriend texts a male friend, the boyfriend asking the question is accused of being "insecure," "possessive" and "toxic" for having boundaries.

O/T Rant: Nowhere are these double standards more apparent than in Are We Dating the Same Guy groups on Facebook. They are the female equivalent of revenge porn run by the female equivalent of incels. Go see the AWDTSGisToxic sub for examples of the kind of hypocrisy that goes on there. If I ran a sentiment analysis on the content of those groups I'm guessing it would be at the same level as any "hate group" online aimed at any segment of the population, from blacks to jews to women. The difference? Everyone sane categorically despises "those" hate groups, but AWDTSG is normalized "for women's safety". Yea right...

Gender Bias in Online Forum Responses 1

Gender Bias in online forum responses 2

Gender Bias in Online Forum Responses 3

Here's a detailed breakdown of the methodology used in this study:

1. Data Collection

   def fetch_reddit_responses(query, subreddits=['relationship_advice', 'relationships'], limit=5): """ - Searches specified subreddits using Reddit's JSON API - Collects both posts and top comments - Uses paired queries (e.g., "boyfriend messages" vs "girlfriend messages") - Implements rate limiting and error handling """

2. Search Structure

   [ { "scenario": "messages_others_boyfriend", "variant1": "My boyfriend messages other girls", "variant2": "boyfriend messaging other girls" }, { "scenario": "messages_others_girlfriend", "variant1": "My girlfriend messages other guys", "variant2": "girlfriend messaging other guys" } ]

3. Data Points Collected Per Post:

• Post/comment text
• Response type (post/comment)
• Reddit score (upvotes)
• URL for reference
• Scenario category
• Gender variant

1. Sentiment Analysis

   Using VADER (Valence Aware Dictionary and sEntiment Reasoner) sia = SentimentIntensityAnalyzer() def analyze_sentiment(text): """ Returns compound score between -1 (most negative) and +1 (most positive) Considers: - Word choice and punctuation - Capitalization and modifiers - Context-specific sentiment """ return sia.polarity_scores(text)

2. Sample Size:

• Total responses analyzed: 825
• Unique posts: 140
• Unique comments: 685
• Per scenario: ~60 responses
• Per gender variant: ~30 responses

1. Limitations:

• Reddit demographic bias
• English language only
• Subreddit-specific culture
• Self-reporting bias
• Time period limitations (recent posts only)

1. Quality Control Measures:

   def safe_request(url, max_retries=3): """ - Implements retry logic - Rate limiting (2-4 second delays) - Error handling and logging - Response validation """

2. Percentage Calculation Method:

   def calc_sentiment_diff(group1, group2): """Calculate percentage difference between two groups""" diff = ((group1 - group2) / abs(group2)) * 100 if group2 != 0 else np.inf return diff # Example: messages_bf = df[df['scenario'].str.contains('messages_others_boyfriend')]['compound_score'].mean() messages_gf = df[df['scenario'].str.contains('messages_others_girlfriend')]['compound_score'].mean() difference_percentage = calc_sentiment_diff(messages_bf, messages_gf)

3. Data Storage:

   CSV structure columns = [ 'scenario', 'variant', 'query', 'response_text', 'response_url', 'response_type', 'response_score', 'compound_score', 'positive_score', 'negative_score', 'neutral_score' ]

4. Potential Sources of Bias:

• Search term selection
• Reddit's sorting algorithm
• Comment selection (top 5 per post)
• VADER's sentiment analysis limitations
• Temporal bias (recent posts weighted more heavily)

1. Validation Methods:

• Cross-referencing multiple search terms
• Comparing post and comment sentiments
• Manual review of extreme scores
• URL preservation for verification