cs.CLOct 3, 2026
SaveHow Do People Challenge Racial Stereotypes Online? Counter-Story Detection Across Reddit Communities
Organizations: IBM · Carnegie Mellon University · University of Colorado Boulder
Abstract
Counter-storytelling is a powerful mechanism people use to challenge dominant narratives. Unlike other forms of counterspeech that have been widely studied in computational social science, counter-storytelling has largely been overlooked. Counter-stories are difficult to detect automatically; they are relational (defined with respect to expressions of racial stereotypes) and structurally diverse (drawing on stories that describe lived experiences, witnessed events, exemplars, and hypotheticals). We introduce a first framework for detecting and characterizing counter-storytelling against racial stereotypes at scale. This includes (1) a three-dimensional taxonomy grounded in narratology and Critical Race Theory and (2) a multi-stage pipeline that identifies relational pairs of stereotypes and counter-stories in noisy Reddit discourse. Using this pipeline, we annotate 25,549 Reddit posts across 615 communities and identify 1,312 counter-stories. Our analysis shows that speaker identity and post context shape how counter-stories are told. For example, in-group writers favor first-person testimony, often adopting the role of self-reflective insiders. Our work shows how computational methods can scale qualitative approaches to identify and characterize counter-storytelling as a contextual narrative practice, with implications for content moderation, narratology, and racial discourse analysis.
Figures & tables
Figure 1: A motivating example of a counter-story responding to a stereotype-containing Reddit post (highlighted in red) on r/unpopularopinion . The example highlights the difficulty of identifying counter-storytelling: the narrator establishes a narrative position by recounting firsthand experience, asserts in-group identity , and directly addresses the original poster ( audience ). They also invert the racial premise of the stereotype and reframe the stereotypical perception as mental conditioning rather than a fact about the group.
| Type | Label | # | % |
| Post | Stereotypes | 3,600 | 14.1 |
| Asian | 1,198 | 33.3 | |
| Black | 868 | 24.1 | |
| Hispanic | 71 | 2.0 | |
| Other | 1,463 | 40.6 | |
| Non-stereotypes | 21,949 | 85.9 |
Table 1: Overview of the LLM annotated dataset.
| Narrative Position | Audience | Identity | |||||||||
| Domain | #Subs | #CS | CS% | Auto- diegetic | Hetero- diegetic | Homo- diegetic | Original Poster | General/ Mixed | Victim Group | Target Group | Dominant Group |
| Culture/Identity | 66 | 430 | 20.9% | 68% | 8% | 12% | 98% | 86% | 35% | 66% | 5% |
| Advice/Education/Work | 38 | 300 | 20.3% | 72% | 9% | 10% | 99% | 80% | 23% | 33% | 17% |
| Debate/Opinion | 7 | 200 | 36.4% | 34% | 30% | 14% | 98% | 98% | 0% | 10% | 14% |
| General/Ask | 19 | 140 | 24.0% | 54% | 15% | 19% | 100% | 99% | 3% | 26% | 31% |
| Stories | 13 | 93 | 12.3% | 65% | 8% | 12% | 98% | 80% | 25% | 25% | 28% |
Table 2: Counter-stories grouped by community domain, ordered by counter-story count. #Subs : subreddits with counter-story. #CS : total counter-stories. CS% : share of comments labeled counter-stories. Audience is multi-label, so columns may sum 100%. The remaining narrative positions (hypothetical, metadiegetic) are reported in App. Table 23 . Speaker’s gender and race breakdowns are reported separately in Table 16 and Table 21 .
| Stereotype | Counter-Story | |||
| Annotator | Agr.% | Agr.% | ||
| Human | 86 | 0.73 | 82 | 0.64 |
| Sonnet 4.6 | 88 | 0.76 | 86 | 0.72 |
Table 3: Inter-annotator agreement (Fleiss’ ) across three annotators and model agreement with the human majority vote (Cohen’s ), on 50 stereotype posts and 100 counter-story comments.
| Stereotype Type | Asian | Black | Other |
| Cultural | 74.3% | 52.6% | 76.5% |
| Appearance | 10.6% | 12.4% | 5.7% |
| Criminality | 2.9% | 13.6% | 6.6% |
| Dehumanization | 4.7% | 9.4% | 3.9% |
| Other | 3.4% | 6.8% | 2.9% |
| Intelligence | 3.2% | 3.1% | 1.8% |
Table 4: Distribution of stereotype types by target group (column-wise within each group).
Figure 2: Pointwise mutual information (PMI, ) between stereotype context and narrative dimensions across all the 1,312 counter-stories. Orange indicates feature pairs that co-occur more than chance; blank cells indicate no co-occurrence. Gray cells denote pairs with fewer than five co-occurrences ( ).
Figure 3: Pointwise mutual information (PMI, ) between speaker context and narrative dimensions across all the 1,312 counter-stories. Orange indicates feature pairs that co-occur more than chance; blank cells indicate no co-occurrence. Gray cells denote pairs with fewer than five co-occurrences ( ). Additional feature pairs are reported in App. § E (Fig. 9 ).
| Role | % | |
| Outgroup Ally | 344 | 26.2 |
| Self-Reflective Insider | 276 | 21.0 |
| Example Provider | 221 | 16.8 |
| Empathetic Reframer | 207 | 15.8 |
| Ingroup Mentor | 173 | 13.2 |
| Outgroup Reassurer | 52 | 4.0 |
Table 5: Distribution of counter-stories across the seven counter-storytelling roles identified by LCA. Total .
Appendix figures & tables27 assets
Supplementary material from the paper’s appendix.
Appendix
| Category | Example Terms |
| Identity keywords | black, african american, bipoc, african, african descent, afro-american |
| Dog-whistles | jogger(s), ghetto(s), urban, welfare queen(s), thug(s), black-on-black crime, absent fathers, 13%, 13/50 |
| Slurs | [We include 600+ slurs in our lexicon but omit them here due to their highly offensive nature. Full list available upon request for research purposes.] |
Table 6: Black/African race lexicon. Identity keywords: ; Dog-whistles: ; Slurs: .
| Category | Example Terms |
| Identity keywords | latino, latina, latinx, hispanic, mexican american, chicano, puerto rican, cuban, central american, south american |
| Dog-whistles | secure the border, illegal aliens, criminal aliens, anchor baby/babies, stealing jobs, cartel, deport them all, press 2 for spanish, mexifornia |
| Slurs | [We include 170+ slurs in our lexicon but omit them here due to their highly offensive nature. Full list available upon request for research purposes.] |
Table 7: Hispanic race lexicon. Identity keywords: ; Dog-whistles: ; Slurs: .
| Category | Example Terms |
| Identity keywords | asian, asian American, east asian, south asian, southeast asian, indian, chinese, japanese, korean, vietnamese, filipino, pakistani, bangladeshi, indonesian |
| Dog-whistles | model minority, china virus, kung flu, wuhan virus, foreigner, tiger mom, abolish birthright citizenship, ccp influence, academic cheats, tech job thieves, high iq, stem overlords |
| Slurs | [We include 340+ slurs in our lexicon but omit them here due to their highly offensive nature. Full list available upon request for research purposes.] |
Table 8: Asian race lexicon. Identity keywords: ; Dog-whistles: ; Slurs: .
Figure 4: Our three-dimensional taxonomy for characterizing counter-stories: Narrative Position captures how the narrator formally relates to the story, Audience captures who the counter-story addresses; comments may address multiple audiences, and Identity captures how the speaker positions themselves relative to the stereotyped group, including self-disclosed demographic attributes.
Figure 6: Overview of the multi-stage data filtering pipeline.
| Group | Sources |
| Black | Muhammad (2010) ; Harris-Perry (2011) ; Gates (2018) ; Taylor and Austen (2012) ; Tucker (2009) ; Bogle (2001) |
| Asian | Wu (2013) ; Lee (1999) ; Markus and Moya (2010) ; Chakravorty (2014) |
| Hispanic | Chavez (2013) ; Bebout (2016) ; Alonzo (2009) ; Limón (1973) |
Table 9: Sources from which we drew dominant-stereotype concepts. Concepts and carrier sentences were constructed from stereotype expressions documented in these works.
| Group | Concepts |
| Black (20) | personality, crime, music, athletic, skills, appearance, socioeconomic, family relations, intelligence, welfare/laziness, absent fathers, AAVE mockery, angry, anti-intellectual, colorism, food (chicken/watermelon), gang violence, kinky hair, hypersexualization, racial assimilation |
| Asian (27) | model minority, academics, tiger mom, language barrier, perpetual foreigner, appearance, dehumanization, COVID blame, eating dogs/cats, emasculation (men), fetishization (women), ladyboy trope, martial arts, orientalist, asian drivers, socioeconomic, work ethic, family structure, SE-Asian maid/nanny, SE-Asian mail-order bride, S.Asian IT scammer, S.Asian accent, S.Asian arranged marriage, S.Asian body hair, S.Asian caste, S.Asian curry smell, Vietnam-war tropes |
| Hispanic (19) | immigration, crime, socioeconomic, economic impact (job-stealing), sexualization, culture, drug cartel, lazy/siesta, anchor babies, machismo, catholic large family, cuban hardliner, latino welfare, narco aesthetics, puerto rican violence, spanglish/uneducated, spicy latina, stealing jobs, wetback-coded |
Table 10: Full inventory of the 66 stereotype concepts (20 Black, 27 Asian, 19 Hispanic). Each concept has 10 carrier sentences (5 implicit, 5 explicit). The complete carrier-sentence set is released with our code.
| Concept | Implicit | Explicit |
| African | ||
| Personality | They always seem to avoid work. | They are lazy. |
| You can never talk to them without a fight starting. | They are angry. | |
| You’ll hear them before you see them. | They are so loud. | |
| Funny how they always pick the same political side. | They are racist. | |
| They don’t exactly fit into polite society. | They’re uncivilized. | |
Table 11: Implicit and explicit carrier sentences for five representative Black stereotype concepts (of 20). The full set of 660 carrier sentences across all 66 concepts is released with our code.
| Concept | Implicit | Explicit |
| Asian | ||
| Model Minority | Of course she aced the math test. | They are really good at math. |
| He’s the quiet type. | They are well behaved. | |
| I bet their kid is doing calculus. | They are calm. | |
| You never have to worry about them. | They are reserved. | |
| He doesn’t speak up much. | They are so quiet. | |
Table 12: Implicit and explicit carrier sentences for five representative Asian stereotype concepts (of 27). The full set of 660 carrier sentences across all 66 concepts is released with our code.
| Concept | Implicit | Explicit |
| Hispanic | ||
| Immigration | They threaten our national security. | They are crossing the border illegally. |
| They show up without papers. | They don’t know any English. | |
| Their children are born here for citizenship. | They are a threat to national security. | |
| They don’t even try to learn the language. | They have anchor babies. | |
| They’re always finding ways around the system. | ||
Table 13: Implicit and explicit carrier sentences for five representative Hispanic stereotype concepts (of 19). The full set of 660 carrier sentences across all 66 concepts is released with our code.
| Stereotype type | Auto | Hete | Homo | Hypo | Meta |
| Cultural (n=1,018) | 63.5 | 11.3 | 13.1 | 2.5 | 9.4 |
| Appearance (n=105) | 61.9 | 7.6 | 14.3 | 1.9 | 14.3 |
| Criminality (n=83) | 39.5 | 37.0 | 8.6 | 11.1 | 3.7 |
| Intelligence (n=33) | 51.5 | 27.3 | 6.1 | 9.1 | 6.1 |
| Economic (n=25) | 40.0 | 24.0 | 16.0 | 8.0 | 12.0 |
| Dehumanization (n=16) | 43.8 | 31.2 | 12.5 | 12.5 | 0.0 |
Table 14: Stereotype type narrative position among counter-stories (row %; per type shown). Raw proportions underlying the associations in Fig. 2 .
| Stereotype Type | Target Group | |||||||||||||
| Domain | #Subs | #Stereo. | Stereo.% | Cult. | App. | Crim. | Deh. | Int. | Eco. | Oth. | Asian | Black | Hisp. | Other |
| Culture/Identity | 139 | 988 | 18.5% | 85% | 4% | 4% | 2% | 2% | 1% | 2% | 40% | 10% | 1% | 50% |
| Advice/Education/Work | 91 | 796 | 12.0% | 73% | 10% | 6% | 2% | 3% | 2% | 4% | 41% | 19% | 2% | 39% |
| Stories | 28 | 420 | 16.7% | 70% | 10% | 6% | 8% | 1% | 1% | 3% | 30% | 31% | 2% | 37% |
| Debate/Opinion | 15 | 384 | 34.5% | 58% | 6% | 18% | 5% | 4% | 3% | 6% | 13% | 57% | 1% | 30% |
| General/Ask | 27 | 345 | 14.3% | 64% | 19% | 3% | 5% | 2% | 1% | 6% | 37% | 25% | 6% | 33% |
Table 15: LLM-annotated posts that contain a stereotype, by community domain, stereotype type, and target racial group, ordered by stereotype count. #Stereo. : number of posts labeled as containing a stereotype; Stereo.% : share of the domain’s posts that contain a stereotype. Cult. =cultural, App. =appearance, Crim. =criminality, Deh. =dehumanization, Int. =intelligence, Eco. =economic, Oth. =other. Rows may not sum to exactly 100% due to rounding.
| Gender | n | Auto | Hete | Homo | Hypo | Meta |
| Woman | 208 | 87.0 | 0.5 | 2.9 | 0.0 | 9.6 |
| Man | 199 | 82.9 | 1.0 | 6.5 | 0.0 | 9.5 |
| Undisclosed | 905 | 49.3 | 19.7 | 16.1 | 4.9 | 9.3 |
Table 16: Author gender narrative position among the 1,312 counter-stories. Percentages are row percentages: e.g., 87% of counter-stories written by women use autodiegetic narration. Gender is disclosed in 31% of counter-stories ( ).
| Posts | Comments | |||
| Backbone | Agr.% | Agr.% | ||
| Gemma-3-27B | 69 | 0.36 | 60 | 0.27 |
| OLMo-2-32B | 86 | 0.73 | 68 | 0.41 |
| Qwen3-32B | 85 | 0.69 | 77 | 0.56 |
| Claude Sonnet 4.6 | 88 | 0.76 | 86 | 0.72 |
Table 17: LLM backbone comparison. Table shows agreement with human annotations on the validation set (50 posts, 100 comments). Claude Sonnet 4.6 (our pipeline backbone) shows the strongest agreement on both tasks.
| Task | P | R | F1 | Acc |
| Stereotype | 0.93 | 0.84 | 0.89 | 0.88 |
| Counter-story | 0.81 | 0.88 | 0.84 | 0.86 |
Table 18: Precision, recall, F1, and accuracy of Claude Sonnet 4.6 against human-majority labels on the validation set.
| Certainty | Ster. % | Ster. Acc. | CS % | CS Acc. |
| 1–6 | 7 | 75% | 6 | 33% |
| 7–8 | 24 | 79% | 30 | 77% |
| 9–10 | 69 | 93% | 64 | 95% |
Table 19: Accuracy by Sonnet’s self-reported certainty on the validation set. Columns show the share of items in each certainty bin and the accuracy within it. Point-biserial correlation between certainty and correctness: (stereotype), (counter-story).
Figure 7: BIC across – for the LCA over the taxonomy features; BIC is minimized at . We report seven roles after merging two adjacent classes.
| Subreddit | #Story | #CS | CS% |
| r/changemyview | 183 | 129 | 70.5 |
| r/unpopularopinion | 274 | 62 | 22.6 |
| r/politicalhumor | 10 | 3 | 30.0 |
| r/conspiracy | 42 | 2 | 4.8 |
| r/tiadiscussion | 5 | 2 | 40.0 |
| r/negareddit | 2 | 1 | 50.0 |
Table 20: Counter-story rate (CS%) by subreddit within the Debate/Opinion domain. #Story = story-containing comments; #CS = counter-stories.
| Race | n | Auto | Hete | Homo | Hypo | Meta |
| Asian | 371 | 77.1 | 3.8 | 8.1 | 0.3 | 10.8 |
| White | 162 | 72.2 | 3.7 | 11.1 | 0.6 | 12.3 |
| Black | 95 | 80.0 | 3.2 | 5.3 | 3.2 | 8.4 |
| Hispanic | 52 | 69.2 | 3.8 | 7.7 | 0.0 | 19.2 |
| Other | 45 | 71.1 | 4.4 | 11.1 | 0.0 | 13.3 |
| Undisclosed | 587 | 41.7 | 26.2 | 17.5 | 6.6 | 6.6 |
Table 21: Author race narrative position among the 1,312 counter-stories (row percentages). Race is disclosed in 55% of counter-stories ( ).
Figure 8: Narrative position by writer identity (left) and target group (right), as a proportion of counter-stories within each group.
Figure 9: Complete pointwise mutual information (PMI, ) between counter-story features and the audience and narrative-subtype categories not shown in the main figures, run over the 1,312 counter-stories, extending Figures 2 and 3 . Rows cover the writer’s group membership, connection to the target group (a personal tie without membership), disclosed race and gender, and stereotype type; columns cover the remaining audience categories (original poster, general/mixed) and the heterodiegetic narrative subtypes (second-hand, exemplar, fictional). Orange cells indicate feature pairs that co-occur more than chance. Gray cells denote pairs with fewer than five co-occurrences ( ), which we omit to avoid unstable estimates from sparse pairs.
| Category (#subs) | Example subreddits |
| Misc (140) | r/tipofmytongue, r/mildlyinfuriating, r/news, r/worldnews, r/mildlyinteresting |
| Culture/Identity (139) | r/asianparentstories, r/india, r/indiaspeaks, r/actuallesbians, r/mexico |
| Advice/Edu/Work (91) | r/depression, r/tooafraidtoask, r/legaladvice, r/dating, r/relationship_advice |
| Geography (79) | r/asknyc, r/houston, r/sandiego, r/casualuk, r/chicago |
| Hobbies (42) | r/squaredcircle, r/nba, r/cfb, r/trees, r/tattoos |
| Stories (28) | r/amitheasshole, r/trueoffmychest, r/fatpeoplestories, r/offmychest, r/vent |
Table 22: Community categories and example member subreddits, ordered by number of member subreddits. We show five most frequent subreddits per category.
| Domain | #CS | Hypo- thetical | Meta- diegetic | Second- hand | Exemplar | Fictional | Connection to Target Group | Membership Undisclosed |
| Culture/Identity | 430 | 1% | 10% | 10% | 4% | 1% | 12% | 31% |
| Advice/Education/Work | 300 | 2% | 6% | 8% | 3% | 1% | 29% | 56% |
| Debate/Opinion | 200 | 8% | 13% | 10% | 28% | 4% | 20% | 76% |
| General/Ask | 140 | 1% | 11% | 14% | 6% | 1% | 26% | 50% |
| Stories | 93 | 5% | 11% | 12% | 6% | 0% | 23% | 54% |
| Geography | 53 | 8% | 11% | 13% | 11% | 0% | 21% | 74% |
Table 23: Counter-story narrative positions and subtypes omitted from Table 2 (row % of #CS), by community domain: hypothetical and metadiegetic narration; the second-hand, exemplar/celebrity, and fictional subtypes; the share of writers reporting a personal connection to the target group; and the share whose group identity is undisclosed (neither target- nor dominant-group membership disclosed), complementing the Target/Dominant columns in Table 2 .
| Stereotype Type | Asian | Black | Hispanic | Other |
| Cultural | 74.3% | 52.6% | 73.2% | 76.5% |
| Appearance | 10.6% | 12.4% | 5.6% | 5.7% |
| Criminality | 2.9% | 13.6% | 11.3% | 6.6% |
| Dehumanization | 4.7% | 9.4% | 5.6% | 3.9% |
| Other | 3.4% | 6.8% | 2.8% | 2.9% |
| Intelligence | 3.2% | 3.1% | 0.0% | 1.8% |
Table 24: Full stereotype-type distribution across all race-groups.
Figure 10: Logistic-regression coefficients (log-odds) for the model using both context and speaker predictor predicting whether a story comment is counter-story or not. Bars with are significant after Holm–Bonferroni correction ( ).
Figure 11: Logistic-regression coefficients (log-odds) for the model using context predictors only to predict whether a story comment is counter-story or not. Bars with are significant after Holm–Bonferroni correction ( ).
Figure 12: Logistic-regression coefficients (log-odds) for the speaker-only model predicting counter-story status. Bars with are significant after Holm–Bonferroni correction ( ).