Automatically Discovering How Misogyny is Framed on Social Media (2025)

Abstract: Misogyny, which is widespread on social media, can be identified not only by recognizing its many forms but also by discovering how misogyny is framed. This paper considers the automatic discovery of misogyny problems and their frames through the Dis-MP&F method, which enables the generation of a data-driven, rich Taxonomy of Misogyny (ToM), offering new insights in the complexity of expressions of misogyny. Furthermore, the Dis-MP&F method, informed by the ToM, is capable of producing very promising results on a misogyny benchmark dataset.

## introduction abusive language used on social media is pervasive, becoming a wide-spread phenomenon that has serious consequences for its victims. misogyny, defined as hatred of, aversion to, or prejudice against women, is no exception. it is important to identify misogyny in social media postings (smps), not only for protecting women, but also for eventually generating counter narratives. however, the identification of misogyny is complicated by the fact that @xcite there are many forms of misogyny, each addressing another misogyny problem (mp), e.g. woman shaming, woman stereotyping, etc; and (2) each misogyny problem can be framed in multiple ways. figure 1 illustrates two different misogynistic smps , addressing the same mp, namely women stereotyping. women stereotyping is defined by the application of broad, oversimplified, and often negative generalizations about women based on their gender. an examination of the content of the smp shown in figure 1 (a) reveals that it evokes the first frame of misogyny (fom) illustrated in figure 1 , namely fom 1 , while the smp from figure @xcite evokes the second fom, namely fom 2 . while the automatic detection of mps is important for understanding the different forms of misogyny that are spreading on social media, the detection of the way misogyny is framed is also important, as it informs counter narratives addressing misogyny, cf @xcite . unlike previous work @xcite @xcite that detects misogyny in social media by focusing on the recognition of the mps addressed in smps, we are also interested in the discovery of the foms that articulate the cause of the mp. to discover foms like those shown in figure 1 , we considered the widely cited definition of framings, @xcite , which notes that "to frame is to select some aspects of a perceived reality and make them more salient in a communicating text, in such a way as to promote problem definition, causal interpretation, moral evaluation, and/or treatment recommendation for the item described." based on this definition, the mps represent the aspects of misogyny that receive a causal interpretation in the articulation of focs. recently, (weinzierl and harabagiu, 2024) have introduced a method for automatically discovering and articulating frames of communication evoked in smps. however, this method, based on chainof-thought (cot) prompting @xcite of large language models (llms) assumes that we are aware of all the mps. however, an inspection of the previous work aiming misogyny detection indicates that each method has considered a different set of mps, and some have even annotated taxonomies of mps, e.g. @xcite . consequently, there is no consistent or complete set of mps. in this paper we present a method that discovers misogyny problems and the way misogyny is framed on social media by ( 1 ) identifying automatically all the mps addressed in a dataset of smps; @xcite generating a taxonomy of mps; and (3) articulating all the foms addressing each mp, as evoked in smps. we call this method dis-mp&f, allowing us to make the following contributions: ¡1£ we introduce the first automatic method that identifies all the misogyny problems (mps) that are discussed in a collection of smps, by prompting a large multimedia model (lmm). the few-shot framework used by the prompting methods of dis-mp&f eliminates the need of expensive annotations of various forms of misogyny on collections of smps. ¡2£ we describe the first method of automatically organizing mps into taxonomies, which resulted in the largest misogyny taxonomy yet considered. ¡3£ we present first method that is able to discover and articulate frames of misogyny (foms) without being aware of all the mps discussed in a collection of smps. furthermore, this method produces new state-of-the-art (sota) results on a benchmark dataset. we make available all discovered mps, the taxonomy of misogyny (tom) and the foms that were discovered, as well as all prompts that we used on github a taxonomy for misogyny labeling applied to a corpus of primarily english reddit posts was presented in @xcite . in this taxonomy, there are four overarching mps: (i) misogynistic pejoratives, (ii) descriptions of misogynistic treatment, (iii) acts of misogynistic derogation and (iv) gendered personal attacks against women. methods based on bert @xcite were able to identify mps on this dataset with accuracy of 93% and f1-score pf 43%. the semeval-2022 task 5: multimedia automatic misogyny identification (mami) (fersini @xcite of the images present in the smps combined with text encoding generated with lstm-based sequential models. in contrast, in this paper we address the problem of discovering all the mps that can be inferred from a dataset of smps. for this reason, we use several promptings of an lmm and of an llm to discover not only the mps addressed throughout smps, but also the foms that are evoked. in addition, we generate automatically the largest taxonomy of mps, which we derived from the mami dataset. ## the dataset the mami dataset, released for the semeval-2022 task 5: multimedia automatic misogyny identification (mami) @xcite ) contains 11,000 smps using memes, divided into a training dataset containing 10,000 memes; and a test set containing 1,000 memes. the smps originate from various social media platforms like x / twitter, reddit, 9gag, knowyourmeme, and imgur. the smps were annotated with four mp labels: ⋄1⋄ shaming, which is defined as the practice of criticising women who violate expectations of behaviour and appearance regarding issues related to gender typology or related to physical appearance @xcite . ⋄2⋄ stereotyping, which is defined as the practice of assigning a fixed, conventional idea or set of characteristics to a woman. the mami annotations considered a stereotype as a fixed, conventional idea or set of characteristics assigned to a woman @xcite . a total of 7,220 of the 11,000 smps were annotated with mps. a closer inspection of these annotations indicated that 2893 of the smps were annotated with only one mp, while 1342 smps were annotated with two different mps. a total of 475 smps were annotated with three different mps and only 475 smps were annotated with all four mps considered in this dataset. the inter-annotator agreement of the labeling produced in mami, as reported in @xcite , was computed using the the fleiss kcoefficient @xcite , resulting in a value of 0.3373. this very low value of the k-coefficient motivated us to consider a misogyny discovery method that does not have to rely on these annotations, but that can instead use the reasoning capabilities of lmms to discover the mps as well as the foms. ## the method the dis-mp&f method operates in three phases: • in phase 1, it discovers and articulates foms by using chain-of-thought (cot) prompting @xcite of a large multimedia model (lmm). ## experimental results in our experiments we employed gpt-4o as the lmm prompted with cot f om , for discovering and articulating foms in phase 1 of dis-mp&f. we also used used gpt-4o as the llm prompted in phase 2 of dis-mp&f (a) with the cot c_m p prompt to correct the extracted mps; (b) with the cot a prompting, which consolidates the mps into l c m p ; and (c) with cot h prompt to discover all hierarchical relations between mps, informing the tom. gpt-4o was again used in phase 3 of dis-mp&f with the cot rel prompt, which led to the discovery of relations between foms. all prompting experiments were performed through the ope-nai api, utilizing the same hyperparameters: 512 max generated tokens, a temperature of 1.0, and 0.7 top-p sampling. quantitative results: phase 1 of dis-mp&f resulted in the articulation of 9,192 foms. in phase 2 of dis-mp&f 224 mps were extracted from these foms. 48 of them were corrected due to cot c_m p prompt and 77 were found to be analogous, resulting in a list of 99 unique mps. these mps were organized in the tom due to the discovery of 88 problem-subproblem relations facilitated by the cot h prompt. the tom has a maximum depth of 3, but it also contains 11 unique tops of the hierarchy, therefore greatly enhancing the understanding of misogyny through its problems, from the original four mps considered in the mami dataset. the foms are spanned by 15,504 paraphrase relations and 55 contradiction relations, discovered when using cot rel . after eliminating paraphrases, the final set of foms was reduced to n @xmath0. therefore the tom contains 99 mps across 11 hierarchies as well as 758 foms, each connected to some mp from the tom. ## discussion the automatically derived tom is providing unprecedented detailed knowledge about how misogyny is discussed on social media, by organizing 99 different mps, backed by academic literature, into 11 different hierarchies and showcasing in how many ways each mp is framed. in addition to the 99 mps, we can inspect 758 foms. interestingly, it seems that the hierarchy of patriarchal attitudes dominates in terms of the number of mps it covers, as shown in figure 6 . the dis-mp&f method was proven to discover misogyny with sota results, without needing more than a very few examples in the five different forms of cot prompting of lmms/llms. this may be due to the impressive knowledge available in gpt-4o, as well as to its reasoning capabilities. the design and evaluation of the the dis-mp&f addresses a new form of bias, namely point-of-view bias, which was afforded by the specifics of misogyny, in which women are victimized. interestingly, this bias was manifested through (a) incorrect selection of the misogyny problem (mp) by the llm; and (b) the biased rationale produced through cot reasoning. ## conclusion this paper presents an automatic method for generating a taxonomy of misogyny that encodes not only misogyny problems (mps) but also the way misogyny is framed on social media. several ways of employing chain-of-thought prompting of large multimodal models proved to be successful in creating the taxonomy as well as in automatically discovering misogyny on a benchmark dataset with state-of-the-art results.