Lexicools at SemEval-2023 Task 10: Sexism Lexicon Construction via XAI
Year: 2023

Abstract:
This paper presents our work on the SemEval2023 Task 10 Explainable Detection of Online Sexism (EDOS) (Kirk et al., 2023) using lexicon-based models. Our approach consists of three main steps: lexicon construction based on Pointwise Mutual Information (PMI) and Shapley value, lexicon augmentation using an unannotated corpus and Large Language Models (LLMs), and, lastly, lexical incorporation for Bag-of-Word (BoW) logistic regression and fine-tuning LLMs. Our results demonstrate that our Shapley approach effectively produces a high-quality lexicon. We also show that simply counting the presence of certain words in our lexicons and comparing the count can outperform a BoW logistic regression in task B/C and fine-tuning BERT in task C. In the end, our classifier achieved F1-scores of 53.34% and 27.31% on the official blind test sets for tasks B and C, respectively. We, additionally, provide an in-depth analysis highlighting model limitations and bias. We also present our attempts to understand the model’s behavior based on our constructed lexicons. Our code and the resulting lexicons are open-sourced in our GitHub repository https://github.com/ SirBadr/SemEval2023-Task10.

heading: PMI Lexicons
text: 1. Threats, 484 woman., coworkers, lovin, purposely, laughed, zog, replaceable, £20, kosher, genital 2. Derogation, 2,418 threir, indians, freckles, niceguys, master, contain, unshaved, events, 2/3rds, shitfuck 3. Animosity, 1,890 log, misbehaving, homie, limelight, atleast, setbullshit, appearing, nodding, bint, priorities 4. Prejudiced Discussions, 608 sundaymorning, boermeester, english, interviewed, histrionics, stiletto, testify, confirmkavanaugh, tldw, predict

heading: PMI Lexicons augmented with Gab
text: 1. Threats, 635 tate, porks, headfirst, ice, explodes, africans/latinos, drinks, stat, patriarch, hen 2. Derogation, 3,078 4.0, millennials, satisfied, sl00ts, accidently, immorality, inability, koolaid, disconected, congresswomen 3. Animosity, 2,423 lml, quickest, ambush, pal, faggot-ass, deus, leaning, grimace, whiz, whitepages 4. Prejudiced Discussions, 775 coasting, prioritizing, blue-pilled, uncorroborated, fraudulent, coaching, cuckoldry, schrödingers, genius, micheal
PMI Lexicons augmented with GPT-J 1. Threats, 2,505 tweet, continuing, turkey, pee, forward, wrist, asphalt, flawed, massing, stomping 2. Derogation, 4,218 lol, rejections, compensate, pontificating, ma’am, hood, eveything, lurks, parlor, bullshit 3. Animosity, 3,690 hillbilly, 1940s, terror-spawn, bullied, romantically, grotto, exceedingly, ben, pulls, doxx 4. Prejudiced Discussions, 2,526 hypocrite, finest, reads, ads, gentleman, pic, convince, warming, wars. . . , drafted
PMI Lexicons augmented with BERTweet 1. Threats, 1,897 rid, commies, bidding, jewshit-babbling, night, dirt, slap, assaulting/harassing, assaulting/harassing, srs 2. Derogation, 6,576 solace, self-admission, lacy, appointment, kathygriffin, mouthed, ka, explores, guff, societies 3. Animosity, 5,360 pound, exceedingly, domina, stall, 12-bore, male-female, sissy, packaged, seize, pies 4. Prejudiced Discussions, 2,538 transsexualism, girl.cigars, aspect, bets, req, gaymobiles, think/know, gig, compatible, again

heading: Shapley Lexicons
text: 1. Threats, 198 womens, hahaha, skank, capital, saveamerica, islamophobe, bleach, trump’s, voterepublican, feminism 2. Derogation, 587 slutty, dykes, enslave, slag, motivates, cunty, rawdogging, woman’s, cuckservative, curviest 3. Animosity, 499 evedence, thotlife, whore’s, bubblegum, dontmakemelaugh, thotdom, oil, slanderer, cunt, vagene 4. Prejudiced Discussions, 243 enslaving, aesthetically, people’s, allen’s, sprog, feminazi, person, hellyweird, sexuality, hire
Sexism Category N Lexicons
Shapley Lexicons augmented with Gab 1. Threats, 245 cuntbrained, solar, pussies, 1wga, sluttier, lmao, lock, eats, howler, howiwillchange 2. Derogation, 1,049 thermograph, 1966, brittanycovington, obedient, losers, cuhzz, wino’s, evolution, kidnappers, menprovement 3. Animosity, 915 whore’s, chokeslamming, gabfamsuxsshit, hahahahahaha, embar-
rass, loney, somebody, christiansoldiersunite, liberalwhackjob, 1337dchess
4. Prejudiced Discussions, 389 womensequalityday, venker, feminazis, responsable, bunnygirl, kyloren, gynocentrism, women’s, akafemin, ugliest
Shapley Lexicons augmented with GPT-J 1. Threats, 2,266 whos, hag, censor, poppy, deserve, happens, calling, rule, isn’t, twats 2. Derogation, 2,710 thinking, years, cuntishness, fire, neighborhood, female’s, firms, sh, nastiest, op 3. Animosity, 2,508 i’ma, soycuckfucks, trying, reproduce, rapefugees, prior, feministas, whore’s, jumping, slut 4. Prejudiced Discussions, 2,191 man’s, flattery, wasn’t, those, wear, bitchy, from, tweet, greatest, louissjw
Shapley Lexicons augmented with BERTweet 1. Threats, 1,674 feminism, lr, cut, pussies, cunt, list, sex, esp, old, lon 2. Derogation, 5,439 stern, thots, partially, pussy, femi, remarks, fembots, facsimile, foid, curiously 3. Animosity, 4,428 try, fetishes, soap, rapefugees, cre, prostate, blew, current, depite, whored 4. Prejudiced Discussions, 2,277 replies, appalling, cunt, family, womans, keepamericagreat, feel, robots, ;, torture
B Examples of Task C Lexicons
Randomly selected lexicons in each sexism category from all experimental settings. Lexicons that are unique to a particular category are highlighted in bold.
Sexism Category N Lexicons

heading: PMI Lexicons
text: 1.1 Threats of Harm, 257 fill, solution, sexiest, wie, somber, decide, alice, stick, meat,
commies
1.2 Incitement and Encouragement of Harm,
839 dump, tribe, feinstein, apointed, calais, rapists, noose, spilt, rapists, islam
2.1 Descriptive Attacks, 1,772 high-tier, cunty, humanly, tradcucks, competing, negates, librarian, kissless, raft, dyes
2.2 Aggressive and Emotive Attacks,
1,618 pest, midol, cenk, eastern, detested, cultural, anybody, century, she-boon, goodlooking
2.3 Dehumanising Attacks & Overt Sexual Objectification,
701 handmaids, het, 1/8th, swelled, plague, hivemind, superior, septic, bratz, mentions
3.1 Casual Use of Gendered Slurs, Profanities, and Insults, 1,685 closes, she-, refusal, flowed, desperation, b4, creation, disseminate, 2,500, bint
3.2 Immutable Gender Differences and Dender Stereotypes,
1,246 friendships, score, confidence, wxmxn, wnba, finds, manhood, bodice-ripper, starve, initiated
3.3 Backhanded Gendered Compliments,
273 wakes, gym, pleasure, skinny, aww, tier, tip, suit, virgins, pleasure
3.4 Condescending Explanations or Unwelcome Advice,
237 k., boss, monitor, cat, anecdotes, card, indifferent, step, eyes, oppress
4.1 Supporting Mistreatment of Individual Women,
396 ideas, hellyweird, bombshell, brutally, schrödingers, practice, whooped, guilt, dc
4.2 Supporting Systemic Discrimination Against Women as a Group,
938 innovator, insure, spaces, reads, challenged, activated, journalist, applying, succeeds, ’equality

heading: PMI Lexicons augmented with Gab
text: 1.1 Threats of Harm, 301 harmed, smack, topic, hamburger, turkey, gook, kike, ap-
ple, accident, feet
1.2 Incitement and Encouragement of Harm,
1,005 arguably, slavic, cellmate, dominance, matbe, negging, sufficiently, encourage, several, nigga
2.1 Descriptive Attacks, 2,283 hobby, donkeys, movement, weaponized, intimate, evening, feminazies, ifs, ’spinsters, gyow
2.2 Aggressive and Emotive Attacks,
2,069 bader, acknowledging, humor, contributed, roastie, misandrists, wind, shaped, stillshepersisted, homely
2.3 Dehumanising Attacks & Overt Sexual Objectification,
883 bp, faces, nsfw, shoshanna, shafted, schlong, correction, roastbeef, kebabs, settle
Sexism Category N Lexicons
3.1 Casual Use of Gendered Slurs, Profanities, and Insults, 2,195 newfound, biddy, neighborhoods, transactional, shit-stink, babylon, gabgirls, intellectual, tgirl, navarro
3.2 Immutable Gender Differences and Dender Stereotypes,
1,497 manospere, played, hired, alcoholic, heaven, ultimate, ocean, flaw, stronger, bossy
3.3 Backhanded Gendered Compliments,
356 hotter, form, range, regardless, pays, despised, rental, transfeminine, freetommy, couldnt
3.4 Condescending Explanations or Unwelcome Advice,
305 desexualized, precious, stuck, regular, pink, propaganda, 2-3, cats, empowered, manifestations
4.1 Supporting Mistreatment of Individual Women,
476 plays, kavanaugh, handicapped, w, loud, katy, zero, spit, wakeupamerica, dc
4.2 Supporting Systemic Discrimination Against Women as a Group,
1,156 unfair, objectification, bothered, /s, apparently, finances, wage, purchase, smarten, mandatory
PMI Lexicons augmented with GPT-J 1.1 Threats of Harm, 1,321 worked, asphalt, theres, punch, brat, rubbing, kidding,
smelt, gymnastics, drilled
1.2 Incitement and Encouragement of Harm,
1,829 kicks, haley, chain, 17:16, shields, defeat, swears, arabs, realised, nail
2.1 Descriptive Attacks, 2,719 pasd, olympic, lavish, hesitating, life-like, 80, army, realizing, giddiness, inconceivable
2.2 Aggressive and Emotive Attacks,
2,541 girlfriend, but, ties, swam, man™, christine, mouthed, acknowledging, avoidance, jew-jizz
2.3 Dehumanising Attacks & Overt Sexual Objectification,
1,868 pills, femoids, ponies, 19, moossies, septic, lighter, sterner, import, weed
3.1 Casual Use of Gendered Slurs, Profanities, and Insults, 2,729 robbed, codreanu, outright, martyr, dopey, huh, shite, x, nails, spencer
3.2 Immutable Gender Differences and Dender Stereotypes,
2,318 guppy, perma, dildo, sisters, drsleeper, activities, 1983, feds, low-quality, simplest
3.3 Backhanded Gendered Compliments,
1,241 anyways, muslims, wondering, pussy, qualm, heavier, crow, delusional, delusional, weak
3.4 Condescending Explanations or Unwelcome Advice,
1,120 jesus, period, mechanism, club, disappear, doubt, finding, comfort, neighborhood, hookers
4.1 Supporting Mistreatment of Individual Women,
1,566 virginity, uncorroborated, handicapped, teases, choices, teases, loud, evidence, drunk, yup
Sexism Category N Lexicons
4.2 Supporting Systemic Discrimination Against Women as a Group,
2,042 featuring, remove, retire, damaging, projects, surplus, sexualization, wade, heels, carbon
PMI Lexicons augmented with BERTweet 1.1 Threats of Harm, 800 decapitated, napalm, chicken, plant, loomer, laura, tf,
fried, vodka, and/or
1.2 Incitement and Encouragement of Harm,
2,088 defense, crib/pad, curb, sway, be, pee, favour, 17:16, leaders, happening
2.1 Descriptive Attacks, 4,732 womensmarch, garage, conquerors, fuels, abomination, relatives, simulation, realized, ballot, prostitution
2.2 Aggressive and Emotive Attacks,
4,362 fapped, aspects, italy, humor, unmarried, bottom, torture, speeds, chong, natalie
2.3 Dehumanising Attacks & Overt Sexual Objectification,
1,996 pills, brought, scenarios, drain, puas, car, pushing, present, janitor, living
3.1 Casual Use of Gendered Slurs, Profanities, and Insults, 4,598 uncle, michael, negroes, smelling, exposes, wholesome, manginas, nahh, julius, worried
3.2 Immutable Gender Differences and Dender Stereotypes,
3,114 miles, u., weather, wnba, exceptions, patient, lets, writes, subreddit, ex-wives
3.3 Backhanded Gendered Compliments,
874 russian, body, paul, shapeless, coper, developing, guns, minority, form, shag
3.4 Condescending Explanations or Unwelcome Advice,
757 monitor, ironically, x, opposite, wife, guy, say, mechanism, suffering, letting
4.1 Supporting Mistreatment of Individual Women,
1,069 smash, groped, scarily, fe, molested, tie, kebab, shop, justice, testify
4.2 Supporting Systemic Discrimination Against Women as a Group,
2,528 pic, mm, reserved, todays, scares, sweat, protection, bestows, proven, acceptance

heading: Shapley Lexicons
text: 1.1 Threats of Harm, 301 explodes, slapped, speak, cunt, scumbag, yelling, raff, „
force, today
1.2 Incitement and Encouragement of Harm,
864 pussy, strike, hb10, sub, arse, looked, thats, 1950s, cunt, named
2.1 Descriptive Attacks, 1,834 congresswomen, wahmen, trannys, ennemy, pussyhats, functioning, subhuman, selfish, romanian, depreciating
2.2 Aggressive and Emotive Attacks,
1,680 stacies, envy, tramp, 248yrs, virgins, trap, skanko, rule, pussyhat, vagina
2.3 Dehumanising Attacks & Overt Sexual Objectification,
758 else, heard, jep, pedo, dream, foids, weak, silly, sheeit, incels
Sexism Category N Lexicons
3.1 Casual Use of Gendered Slurs, Profanities, and Insults, 1,746 hoe, slut, trannys, whore’s, california, whoredom, sucking, can’t, aids, slanderer
3.2 Immutable Gender Differences and Dender Stereotypes,
1,232 ofc, mars, virgin, initiate, well, haha, flakey, daygame, 2incld, favor
3.3 Backhanded Gendered Compliments,
329 every, wrong, sensible, aren’t, faces, femininity, dontmakemelaugh, bitch, care, hotter
3.4 Condescending Explanations or Unwelcome Advice,
281 play, bullet, their, :), others, fact, pussy, men, making, 25
4.1 Supporting Mistreatment of Individual Women,
452 histrionics, who, girl, whining, some, presidenttrump, choices, but, sucker, night
4.2 Supporting Systemic Discrimination Against Women as a Group,
1,000 reads, women’s, sterilize, femoid, giant, water, genius, stiletto, program, ,
Shapley Lexicons augmented with Gab 1.1 Threats of Harm, 337 speak, whore, virginal, bitch, anblick, purposes, women,
fuckug, extradite, too
1.2 Incitement and Encouragement of Harm,
986 femboy, titwank, unhinged, quality, serenawilliams, says, implication, consensual, neocons, server
2.1 Descriptive Attacks, 2,454 autistic, dye, absolutely, fridayfolkday, earth, exists, guns, minetheth, thursdayreads, women’s
2.2 Aggressive and Emotive Attacks,
2,202 chauvinism, idenitites, suffrance, woman’s, bux, skeletonized, trashy, thots, bitchy, untermensch
2.3 Dehumanising Attacks & Overt Sexual Objectification,
928 kazharian, wear, that’s, humongous, arse’ole, evil, crossdressers, spread, pcos, jada
3.1 Casual Use of Gendered Slurs, Profanities, and Insults, 2,343 ensues, pussyfooting, hahaha, momma, [, chokeslamming, vaginocracy, soccermoms, gabfamsuxsshit, homocommandos
3.2 Immutable Gender Differences and Dender Stereotypes,
1,509 cope, genuinly, kavanuagh, celebrity, tee, dropped, theluckyones, feminime, pschology, styxenhexenhammer
3.3 Backhanded Gendered Compliments,
392 them, females, out, womens, she’s, russian, needs, slap, thighhighs, bover
3.4 Condescending Explanations or Unwelcome Advice,
346 lass, renders, rublish, eddieberganza, hookers, feminismishate, play, feminismishate, url, lady
4.1 Supporting Mistreatment of Individual Women,
535 gabfam, gab, chucklefuck, part, wakepamerica, regretted, pussies, sexuality, accuser, skills
Sexism Category N Lexicons
4.2 Supporting Systemic Discrimination Against Women as a Group,
1,227 hollyweird, disney, dealing, singing, hybristophilia, slut, crossdressers, micheal, ripa, kavs
Shapley Lexicons augmented with GPT-J 1.1 Threats of Harm, 1,323 moving, bitching, stolen, sluts, ......, wish, wet, relationship,
stroked, slut
1.2 Incitement and Encouragement of Harm,
1,800 or, wonder, out, females, prostitutes, cuckolded, uniteamerica, final, damn, it’s
2.1 Descriptive Attacks, 2,748 impulses, undercooked, cunts, prone, interactions, submissive, garbage, operative, and, after
2.2 Aggressive and Emotive Attacks,
2,612 condolences, alimony, woemen, justin, poopoo, perverted, sasour, slender, disposable, sideways
2.3 Dehumanising Attacks & Overt Sexual Objectification,
1,814 idea, teachers, fembots, dehumanized, button, argument, screams, argument, assholes, ’bitch
3.1 Casual Use of Gendered Slurs, Profanities, and Insults, 2,701 tranny, pseudo, dyke, muslim, bitchboi, cuntfused, femoids, bitchy, percentage, heh
3.2 Immutable Gender Differences and Dender Stereotypes,
2,246 lolol, slapped, holidays, mountains, expenctancy, wear, self, total, rapes, retard
3.3 Backhanded Gendered Compliments,
1,215 inflames, possessed, good-guy-with-a-gun, ain’t, blonde, else, wanna, whack, wrong, ;
3.4 Condescending Explanations or Unwelcome Advice,
1,109 symmetry, go, already, wifey, bitches, girlfriend, so, ......., girls, hoe
4.1 Supporting Mistreatment of Individual Women,
1,517 presidenttrump, move, culture, lnyhbt, pissed, defend, choke, normie, bitches, damn
4.2 Supporting Systemic Discrimination Against Women as a Group,
2,006 for, wave, finger, lil, lolgirl1, ignominy, become, legislature, mentioned, straight
Shapley Lexicons augmented with BERTweet 1.1 Threats of Harm, 801 boils, sick, explodes, man, meeting, ki, stories, throwing,
kosher, alice
1.2 Incitement and Encouragement of Harm,
2,087 fucker, cunts, dumbass, live, it, re, impregnate, escalating, coworkers, entry
2.1 Descriptive Attacks, 4,775 female, towers, president, threesome, retarded, apples, wish, pussy, school, manipulated
2.2 Aggressive and Emotive Attacks,
4,444 oniggy, dykes, pan, ate, misandrists, remorse, ici, infest, comic, shameful
2.3 Dehumanising Attacks & Overt Sexual Objectification,
2,001 loyal, effect, they’d, g, all, obviously, sue, gunn, necessity, because
Sexism Category N Lexicons
3.1 Casual Use of Gendered Slurs, Profanities, and Insults, 4,665 femnazis, femoids, shekels, self, slayers, muhahahahahahahahahahahahahahahaha, geeky, muhahahahahahahahahahahahahahahaha, shows, tranny
3.2 Immutable Gender Differences and Dender Stereotypes,
3,101 robber, psychotics, kind, what’s, you’ve, perverted, n, friendzone, ppa, hea
3.3 Backhanded Gendered Compliments,
878 behind, miniskirts, getting, trumps, younger, troops, single, blonde, fat, star
3.4 Condescending Explanations or Unwelcome Advice,
759 or, her, tone, count, freedumb, get, x, bitch, she’d, yes
4.1 Supporting Mistreatment of Individual Women,
1,061 du, communicate, two, yup, bitch, bail, better, realize, assumes, equal
4.2 Supporting Systemic Discrimination Against Women as a Group,
2,526 politicians, guy, english, for, guess, femaleprivilege, ideology, rights, done, co
C Analysis on Model Predictions
C.1 Negations This subsection presents more examples of negation that the fine-tuned BERT model misclassifies.
C.2 Bias More examples of bias towards sexism agaist women but ignore LGBTQ+ people.
C.3 Robustness Robustness in machine learning models refers to the ability to maintain consistent behavior and decisions even in the presence of noise. In online content, automatic sexism detectors may misclassify sexist comments as funny comments when it includes some words like “hahaha”, “lol”, and “funny”. To test the robustness of our model against this kind of attack, we added random funny words from a list [hahaha, hehehe, lol lol, lmao lol, lmfao] to the beginning and end of each sentence from test data and checked if the prediction changed. We found that the model maintained its prediction on 3,815 sentences out of 4,000 sentences, or 4.625%. Overall, this finding suggests that while our model is generally robust against noise in the form of noise.
C.4 Lexicon Overlap From Shapley lexicons (without augmentation), we identified a total of 1,335 lexicons. Notably, there is around 4-6% overlap among the sexism classes except for the 2. Derogation and 3. Animosity pair (8.49%), as illustrated in the plot in figure 11 and 12.

heading: Future Work
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Randomly selected lexicons in each sexism category from all experimental settings. Lexicons that are unique to a particular category are highlighted in bold. Sexism Category N Lexicons