Social media can aggravate body dissatisfaction and weight discrimination. Fat-shaming content is widespread on social media and targets people of diferent weights. Fatphobia and its harmful efects afect not only the general public but also children, causing lasting psychological damage. In this work, we make two contributions to the detection of fatphobia in social networks. On the one hand, we compile the Spanish FatPhoCorpus 2023, a multiclass dataset that allows the identification of hate speech, ofensive content, and hopeful messages related to body image. On the other hand, we evaluate this novel dataset with a baseline of several pre-trained models based on Transformers. The best result is obtained with multilingual TwHIN, which achieves a macro average F1-score of 59.158%.
fatphobia (see Section 2.1) and the diferent labels
considered in our study, namely ofensiveness (see Section
2.2), hate speech (see Section 2.3), and hope speech (see language is usually divided into three classes: (i)
“profanSection 2.4). ity”, i.e. the of use of swear words (e.g. fuck), but without
the intention to insult someone; (ii) “insult”, when there
2.1. Fatphobia is a clear intention to ofend someone with disrespect
and contempt; and (iii) “abuse”, when a person is insulted
As far as we know, there is not much work on fatphobia by using a special type of degradation that is considered
from an NLP perspective. One of the papers identified representative of a group by negatively attributing to
was conducted by [
Other existing work deals with fatphobia as a cate- 2018 [16] and GermEval 2019 [17] on German tweets,
gory of hate speech or prejudice. [
It is worth noting that previous work has treated fat- the AMI shared task on the automatic identification of phobia as a category within hate speech. Our work difers misogyny at IberEval 2018 [27] and Evalita 2018 [28], from previous work in that we treat fatphobia as a major the HatEval shared task on the detection of hate speech theme to identify hateful, ofensive or hopeful messages. against immigrants and women [29], and some shared In addition, we focus on the Spanish language. task organized in the framework of the IberLEF workshop [30, 31, 12] on (i) the identification of sexism in 2.2. Ofensiveness social networks, as in EXIST 2021 [32] and 2022 [33], (ii) the detection of toxicity in DETOXIS 2021 [34]), (iii) the detection and classification of racial stereotypes in DETESTS 2022 [35], and (iv) the identification of hate speech towards the LGBTQ+ population in HOMO-MEX 2023 [36].
someone to feel upset or angry because it shows a lack of respect. A text is considered ofensive if it contains any form of unacceptable language, that is, whether it contains insults, threats, or bad language [13]. Ofensive
a hostile environment [37] and that helps, gives suggestions and inspires people for good when they are in times of illness, stress, loneliness or depression [38].
In the current digital age, social media provides a place for people to freely express their views and opinions. They have become critical for people from minority groups seeking help and support online [39, 12]. People have turned to these media to satisfy their informational, emotional, and social needs as they seek to connect with others, experience a sense of social inclusion, and cultivate a sense of belonging through active participation in online communities. The presence of these factors has a profound impact on physical and psychological well-being as well as mental health [40, 41]. This has led to recent research exploring positive content, such as messages of hope, and the promotion of constructive activities in pursuit of equality, diversity, and inclusion.
There has been a remarkable increase in attention to this topic since 2021 and several workshops have been organized to address the challenge of detecting hope speech in English, Tamil, Malayalam, Bulgarian, Hindi and Spanish, such as LT-EDI-EACL2021 [42], LT-EDIACL2022 [43], LT-EDI-RANLP2023 [44] and the shared task HOPE in IberLEF 2023 [12].
The UMUCorpusClassifier tool [ 45] was used to compile the dataset. We started by querying for specific keywords on X (formerly Twitter). The keywords are gordofobia (fatphobia), sobrepeso (overweight), gordo/a (fat), obeso (obese), obesidad (obesity), foca (seal), glotón (glutton), glotonería (gluttony), “talla grande” (plus size), anorexia, lfaco (skinny), “desorden alimenticio” (‘eating disorder), delgado (thin), delgadez (thinnes), and calorías (calories).
The dataset is compiled from all countries where Spanish is spoken. This can be challenging due to diferences in cultural factors between Spanish-speaking countries, resulting in diferent interpretations of adjectives such as “gordo” (fat) or “flaco” (thin). In the first iteration, 39,787 tweets were collected from June 2020 to July 2023, focusing on the specified keywords.
The annotation phase was performed by the research team and an external collaborator. We considered the following labels: (i) Hate, if the text contains hate speech towards people because of their body, (ii) Hope, if the text tends to help, relax, and inspire people for their body weight perception, (iii) Ofensive , if the text contains insults and profanity but no fatphobia, and (iv) None, if none of the rest.
Each tweet was annotated by four members, being two men and two women, members of our research group.
The annotators do not have specific knowledge of the area, but rather linguistics and computer science. The inter-annotator agreement based on Krippendorf’s alpha is 0.712. The final label is assigned based on the mode of the annotations. Ties were resolved in internal meetings.
The analysis of the dataset revealed that some of the offensive tweets contain homophobia, racism or transphobia in addition to fatphobia. Furthermore, some tweets contain irony and figurative language, while others are self-deprecating. Fourth, we observed a lot of internalized fatphobia, as several users wrote that they are very afraid of being fat because they associate being fat with being ugly. However, the main finding is that several tweets used "thin" or "fat" in a familiar way to refer to partners and pets in an afective way and these tweets were labeled as None.
To select the number of tweets that are not related 0% 25% 50% 75% 100% to ofensive, hate, or hope, we use agglomerative clus- Figure 1 shows four examples from the dataset, one for tering with the tweets labeled None. The idea is to each label. The top left example is for a text labeled as get a large but representative number of tweets of dif- hate-speech, which contains fatphobia but also misogyny. ferent types. To do this, we extract their contextual The top right example promotes hope. It is a statement sentence embeddings, obtained by hackathon-pln-es/ from a person who refuses to give its opinion about other paraphrase-spanish-distilroberta, and create five clusters. people’s bodies. The bottom left example is labeled as We then randomly selected one tweet for each cluster None, and it is about a person who regrets eating junk until the cluster with the fewest instances was empty. food and finds it hard to stop. The bottom right example This process reduced the number of tweets marked as is labeled as Ofensive . It is about a person who curses None to 4,570. and insults another man and threatens him with physical
In the final version of the dataset, a total of 6,145 tweets violence. were collected, annotated, and verified. The remaining To analyze the corpus, we used the UMUTextStats tool tweets were removed because they were too short or it [46] to obtain linguistic features to calculate the informawas not clear how to annotate them. These tweets are tion gain for each label. As Figure 2 shows, soft ofensive divided into training, validation, and test sets using a language and swear words, psychological processes re70-15-15 ratio, with stratification to maintain the balance lated to anger, and lexis related to sex show a correlation of labels between the splits. Table 1 shows the statistics with hate speech, and to a lesser extent, with ofensive for each label and split. Note that hope language is the language. The only exception is the food-related termilabel with the lowest representation, while the remaining nology, which is less common in documents labeled as labels have comparable proportions. Hate but is more common in hope speech and fatphobia unrelated tweets. In addition, we observe that analytical Table 1 thinking is used almost equally in all labels. Dataset distribution for label and split. Finally, the dataset is now available to the scientific community1. However, according to the X’s guidelines2, Label Train Val Test Total we will only publish the tweet IDs to protect the users’ right to be forgotten.
2024.zip 2https://developer.twitter.com/en/developer-terms/policy
is usually small, with no steps for ALBETO, BERTIN, DistilBETO and TwHIN.
models for this classification task. For this purpose, the system shown in Figure 3 is developed. In short, the Table 2 Hyperparameter tuning of the seven evaluated LLMs. The pipeline can be described as follows. First, the data pre- parameters evaluated are the learning rate (lr), number of processing stage is performed on the dataset to produce training epochs (e), the batch size (bs), the warm-up steps a proper and clean format, since the quality of the input (ws), and the weight decay (wd). data directly afects the performance and generalizability of the model. In this case, we perform a data cleaning LLM lr e bs ws wd stage where acronyms are expanded, elongations, and ALBETO 2.2e-05 5 8 0 0.21 digits are removed along with hyperlinks, hashtags, quo- BERTIN 1.1e-05 5 16 0 0.11 tation marks, and other punctuation symbols. Finally, we BETO 4e-05 3 8 500 0.23 evaluated diferent pre-trained models based on Trans- DistilBETO 3e-05 4 16 0 0.26 formers and with diferent architectures, such as BERT, MarIA 4.4e-05 4 8 250 0.18 RoBERTa, ALBERT and DistilBERT to classify diferent TwHIN 2.6e-05 3 8 0 0.17 categories using the fine-tuning approach. In this case, RoBERTuito 2.1e-05 5 16 500 0.16 since all the evaluated models are of the encoder type, we have added a sequence classification layer to perform the fine-tuning process.
The fine-tuning process is performed using hyper- 4. Results and discussion
parameter optimization stage to obtain the learning rate
(uniformly sampled between 1e-5 and 5e-5), the num- In the experiments, a simple process is performed by
ber of epochs (between 1 and 5), the batch size ([
The monolingual models evaluated are: (i) BETO [47], and F1-score with the test set. It can be seen that TwHIN a Spanish BERT model trained on the Spanish Unanno- performs quite well in predicting hate speech with an tated Corpora; (ii) MarIA [48], which is a pre-trained F1-score of 64.945%, but the model is less reliable in premodel based on RoBERTa and trained exclusively on dicting hope and ofensive content from a fatphobic perSpanish texts collected from web crawling of the Na- spective. Furthermore, all labels behave similarly in terms tional Library of Spain; (iii) BERTIN [49], another model of precision and recall. based on RoBERTa and trained on the Spanish part of We used TwHIN to perform the error analysis because the mC4 dataset; (iv) ALBETO [50], a pre-trained model it is the fine-tuned model that produced the highest macro based on ALBERT (a lightweight version of BERT), pre- F1-score over the test split. Figure 4 shows the confusion trained only on Spanish documents; (v) DistilBETO [50], matrix for this model, which allows us to identify cases is a model trained using distillation techniques to trans- where the model makes incorrect predictions. We can see fer the weights of BETO to a new model with fewer that the model misclassified 27 instances of hate speech layers and reduced complexity; (vi) RoBERTuito [51], a as ofensive speech. Analyzing these cases, it was found pre-trained model based on RoBERTa for the analysis of that when the level of hate is very high, our model tends social media text in Spanish, and trained according to to identify it as ofensive. It was also observed that the RoBERTa guidelines on 500 million tweets. In terms of analyzed model has dificulties to handle hope speech as multilingual models, TWHIN [52] was evaluated, which it confused 5 instances with hate and 4 with none. The is a multilingual model trained on X covering over 100 case of ofensive speech is also confused with hate (29 languages. cases) and none (21 cases).
In this case, since these are encoder-type models, we It is worth noting that in an earlier version of the anhave added a sequence classification layer to perform notation processes, we identify a large number of posts ifne tuning for a classification task. This layer consists coming from diferent Spanish-speaking countries, inof dense structure with as many neurons as there are cluding Spain, Argentina, Mexico, and so on. In this output classes to classify. To evaluate the classifiers, we sense, there are adjectives with ambiguous meanings, use the macro F1-score as the main benchmark metric, such as “flaco”, which is an informal way for Argentines which weighs the precision and recall of each class and to refer to a man or a woman in an afectionate way, even combines the results without considering the class imbal- if they are not thin. Other examples are the word “gorda” ance. In this way, we select the best model that performs in text 3 and the word “flaco”. In the first version of the equally well for each label. Another reference metric is dataset, these tweets were classified as Hope (leading to the weighted F1-score (W-F1), which is an evaluation false positives), when in fact, they should have been clasmeasure used in classification problems, especially when sified as None. In addition to the cultural background dealing with unbalanced datasets, where some classes of diferent Spanish-speaking countries, we identified may have many more examples than others. Unlike the another challenge related to the fact that people use inmacro F1-score, this metric takes into account the class formal language on social networks, in addition to not imbalance by assigning diferent weights to each class using punctuation or accents correctly. This is particubased on their frequency in the dataset. larly serious in the case of Spanish, as Spanish does not
Table 3 shows the results of all the pre-trained models. change the order of words when moving from declarative In general, all the evaluated models performed equally to interrogative sentences. Often, this meaning can be well in terms of macro and weighted F1-score. The re- taken out of context with considerable human efort, but sults using the weighted precision, recall and F1-score are it makes it very dificult for NLP tools to understand the higher due to the class imbalance and the reliability of all language. models with the None label. Among the evaluated mod- Table 5 shows some examples of misclassifications els, the multilingual TwHIN is the best performing model, made by TwHIN that summarize the challenge of idenwith a weighted F1-score of 83.234% and a macro F1-score tifying hate, hope, and aggressive fatphobic comments. of 59.158%. The monolingual models, BETO and MarIA, The first example contains a message suggesting that based on the BERT and RoBERTa architectures respec- normalizing the message of being okay with one’s body tively, also performed similarly, with a macro F1 score of is counterproductive. This example is dificult to identify 0.603% improvement of MarIA over BETO. Furthermore, because it uses polite language. The second example is it is also worth noting that ALBETO (the lightweight considered ofensive by the model because it contains version of BETO) outperformed distilled BETO (macro many derogatory terms, but it also contains terms related F1-score of 52.159% vs 48.071). In this sense, ALBETO to fatphobia as well as homophobia. The third example, gave similar results to BETO, with a significantly reduced annotated as hope speech, is incorrectly identified by e t a h
words such as because it contains a slang from Chile and Predicted Peru (weon) that refers to stupid and lazy people and because it contains negation cues. The fourth example was identified as None. This is correct because it uses Figure 4: Confusion matrix of the TwHIN model the word flaco as colloquial way. However, it uses the adjective Mongolian, which is used as an insult in Spain.
However, this word contains a typo so it is possible that dataset for the detection of fatphobic comments in soTwHIN does it not consider an ofensive word. The last cial networks in Spanish. This work has considered four example is hate speech, which was misclassified classi- types of comments, according to whether they contain ifed as hope speech. The author is complaining about the hate speech, hope speech, ofensiveness, or none of the so-called crystal generation regarding issues of racism, above. The resulting dataset has been evaluated with fatness and homophobia. In this case, the problem is that diferent pre-trained models based on Transformers, obthe author it does only states a premise but the conclusion taining the best result TwHIN, a multilingual general puris not clear. pose model trained with tweets, with a macro F1 score
Finally, to get some insight into the errors made by of 59.158%.
TwHIN, we get the subset of the test field that was mis- As future work, we will expand the dataset. One limitaclassified and obtain the linguistic features (see Figure tion we found during the evaluation was the dificulty in 5). We observed that the errors contain morphological properly identifying hope and ofensive fatphobic tweets. features, such as interjections, the use of verbs in the im- This is particularly relevant in the case of hope speech, perative and in the infinitive; besides, we also observed where the dataset contains only 84 texts. Furthermore, features related to negativity and that the number of we found that background and cultural diferences are words and syllables is also relevant. very important for detecting fatphobia in social networks. Therefore, we propose the annotation of the Spanishspeaking country of the authors and evaluate the cultural 5. Conclusions diferences when it comes to finding similarities and difIn this paper we have described the compilation and an- ferences between diferent Spanish-speaking countries. notation process of the Spanish FatPho Corpus 2023, a In this sense, we will extend the annotation of the dataset No es por ser gordofobico nada pero amigo en usa tratan de normalizar el "se tu mismo" oh ese estilo de cosas?. Recomiendo ver el video de tri-line hacerca de ese tema pero en mi sincera opinion,no esta mal ser gordo pero es mejor bajar de peso. (It is not because I am fatphobic at all, but my friend in the USA they try to normalize "be yourself" oh that style of things? I recommend watching the Tri-Line video on this topic, but in my honest opinion, it is not bad to be fat, but it is better to lose weight.) Nunca nos olvidaremos a la derecha corrupta llevo a la ruina al país en cabeza del gordo marica q el uribismo puso ahí unas ratas ( We will never forget that the corrupt right led the country to ruin at the head of the fat faggot that Uribismo put some rats there) Esta vez no estoy de acuerdo con su comentario, y debo decir que es bastante weon el comentario, la obesidad se da no solo por comer mucho o una mala alimentacion, deberia informarse antes de decir esa estupidez, y se gano un seguidor menos. (This time I do not agree with his comment, and I must say that the comment is quite bad, obesity is not only caused by eating a lot or bad diet, you should inform yourself before saying that stupid thing, and you gained one less follower.!) Pero flaco ustedes solamente pagan cuota de socio, con eso estás adentro, nosotros abono para NUESTRA cancha, y si no tenés abono y sos socio, tenés q pagar la entrada para ir al kempes, mogolico (But "flaco", you only pay the membership fee, with that you are in, we have a subscription for OUR field, and if you don’t have a subscription and you are a member, you have to pay the entrance fee to go to the Kempes, you idiot.) La generación de cristal es bastante rara. Se molestan cuando uno le dice negro, flaco o gordo a alguien, o cuando se les dice que solo existe hombre o mujer en términos biológicos... (The crystal generation is quite rare. They get upset if you call someone black, thin, or fat, or if you tell them that there is only one biological male or female...) T Ha Ha Ho
P No Of
Ha Of
No Ha
Ho by including volunteers from diferent Spanish-speaking gional Support Program for the Transfer and Valorizacountries. Finally, regarding the evaluation of the mod- tion of Knowledge and Scientific Entrepreneurship of the els, we will evaluate the feature integration to combine Seneca Foundation, Science and Technology Agency of the strengths of diferent pre-trained models based on the Region of Murcia. The research work conducted by Transformers. Salud María Jiménez-Zafra has been supported by Action 7 from Universidad de Jaén under the Operational Plan for Research Support 2023-2024. Mr. Ronghao Pan is Acknowledgments supported by the Programa Investigo grant, funded by the Region of Murcia, the Spanish Ministry of Labour and This work has been partially supported by projects Social Economy and the European Union - NextGeneraLaTe4PoliticES (PID2022-138099OB-I00) funded by MItionEU under the "Plan de Recuperación, Transformación CIU/AEI/10.13039/501100011033 and the European Rey Resiliencia (PRTR)". gional Development Fund (ERDF)-a way of making Europe, LT-SWM (TED2021-131167B-I00) funded by MICIU/AEI/10.13039/ 501100011033 and by the European References Union NextGenerationEU/PRTR, CONSENSO (PID2021122263OB-C21), MODERATES (TED2021-130145B-I00), [1] T. F. Cash, Body image: Past, present, and future, and SocialTOX (PDC2022-133146-C21) funded by Plan 2004.
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