On stereotypes

A stereotype is defined in social psychology as a set of beliefs about others who are perceived as belonging to a different social category. The stereotype oversimplifies the group and generalizes a characteristic, applying it to all its members (Allport et al., 1954). Stereotypes are a cognitive component and, in agreement with prejudice, their emotional counterpart, they model behavior towards others. One way of manifesting stereotypes is through language, ranging in degrees from explicit to implicit expressions, thereby becoming a complex concept when operationalizing it for natural language processing purposes. Stereotypes are expressed in language through several communication acts, which can be explicit, that is, transparent and manifest, or implicit, that is, a process of inference is necessary for the stereotype to be perceived.

Numerous works on stereotype detection and classification have been carried out in which specific social groups, e.g., women and immigrants, have been the focus of research, since they are usually the target of such messages. For instance, Automatic Misogyny Identification (Fersini et al., 2018) presents a classification subtask in which one of the categories of misogyny is Stereotype and Objectification understood as a fixed and oversimplified image or idea of a woman. EXIST (Rodríguez-Sánchez et al., 2021; Rodríguez-Sánchez et al., 2022; Plaza et al., 2023) tackled the topic of sexism in social networks, while more specifically, studies on the detection of gender stereotypes have also been addressed (Cryan et al., 2020; Chiril, 2021). Among the perspectives identifying stereotypes within narratives, there are studies on microportraits, in which a description of Muslim people is provided in a single text (Fokkens et al., 2018). Sap et al. (2020) addresses the issue of social bias frames driven by stereotypes. Evalita 2020’s HaSpeeDe 2 task includes a subtask on the identification of immigrants, Muslims and Roma (Sanguinetti et al., 2020). Narrowing down on the topic of immigration, Sánchez-Junquera et al. (2021) put forward a classification of such stereotypes as manifested in political debates. DETESTS-Dis is therefore an innovative proposal which goes a step further in stereotype identification by incorporating the identification of implicitly expressed stereotypes.

References

Allport, G. W. (1955). The nature of prejudice. Cambridge, Mass: Addison-Wesley Pub. Co.

Chiril, P., Benamara, F. & Moriceau, V. (2021). ‘”Be nice to your wife! the restaurants are closed”: Can Gender Stereotype Detection Improve Sexism Classification?’ In Proceedings of Findings at the 2021 conference on empirical methods in natural language processing (EMNLP).

Cryan, J., Tang, S., Zhang, X., Metzger, M., Zheng, H., and Zhao, B. Y. (2020). ‘Detecting Gender Stereotypes: Lexicon vs. Supervised Learning Methods’. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery, New York, NY, USA, 1–11. DOI: https://doi.org/10.1145/3313831.3376488

Fersini, E., Nozza, D. & Rosso, P. (2018). ‘Overview of the Task on Automatic Misogyny Identification (AMI)’. In Proceedings of the Third Workshop on Evaluation of Human Language Technologies for Iberian Languages (IberEval 2018). http://personales.upv.es/prosso/resources/FersiniEtAl_IberEval18.pdf

Fokkens, A., Ruigrok, N., Beukeboom, C., Gagestein, S., & Van Atteveldt, W. (2019). ‘Studying muslim stereotyping through microportrait extraction’. In H. Isahara, B. Maegaard, S. Piperidis, C. Cieri, T. Declerck, K. Hasida, H. Mazo, K. Choukri, S. Goggi, J. Mariani, A. Moreno, N. Calzolari, J. Odijk, & T. Tokunaga (Eds.), Proceedings of the LREC 2018, Eleventh International Conference on Language Resources and Evaluation (pp. 3734-3741). European Language Resources Association (ELRA). http://www.lrec-conf.org/proceedings/lrec2018/pdf/989.pdf

Plaza, L., Carrillo-de-Albornoz, J., Morante, R., AmigĂł, E., Gonzalo, J., Spina, D., & Rosso, P. (2023). Overview of EXIST 2023: sEXism Identification in Social NeTworks. In European Conference on Information Retrieval (pp. 593-599). Cham: Springer Nature Switzerland.

Rodríguez-Sánchez, F., Carrillo-de-Albornoz, J., Plaza, L., Gonzalo, J., Rosso, P., Comet, M. & Donoso, T. (2021). ‘Overview of EXIST 2021: sEXism Identification in Social neTworks’. Procesamiento del Lenguaje Natural, Vol 67.

RodrĂ­guez-SĂĄnchez, F., Carrillo-de-Albornoz, J., Plaza, L., Mendieta-AragĂłn, A., Marco-RemĂłn, G., Makeienko, M., Gonzalo, J., Spina, D. & Rosso, P. (2022). Overview of EXIST 2022: sEXism Identification in Social neTworks. Procesamiento del Lenguaje Natural, 69, 229-240.

Sánchez-Junquera J, Chulvi B, Rosso P, Ponzetto SP. ‘How Do You Speak about Immigrants? Taxonomy and StereoImmigrants Dataset for Identifying Stereotypes about Immigrants’. Applied Sciences. 2021; 11(8):3610. https://doi.org/10.3390/app11083610

Sanguinetti, M., Comandini, G., Nuovo, E., & Frenda S., Stranisci, M., Bosco, C., & Caselli, T., & Patti, V. & Russo, I. (2020). ‘HaSpeeDe 2 @ EVALITA2020: Overview of the EVALITA 2020 Hate Speech Detection Task’. In Proceedings of Evaluation Campaign of Natural Language Processing and Speech Tools for Italian. http://ceur-ws.org/Vol-2765/paper162.pdf.

Sap, M., Gabriel, S., Qin, L., Jurafsky, D., Smith, N.A., & Choi, Y. (2020). ‘Social Bias Frames: Reasoning about Social and Power Implications of Language’. ACL.