added paper information

README.md

@@ -1,3 +1,36 @@
+This model was released with the following paper:
+```
+@proceedings{feedbackloop,
+  title =        "Feedback Loops and Complex Dynamics of Harmful Speech in Online Discussions",
+  author =       {Rong-Ching Chang, Jonathan May, and Kristina Lerman},
+  publisher =    {Proceedings of the 16th International Conference on Social Computing, Behavioral-Cultural Modeling & Prediction and Behavior Representation in Modeling and Simulation.}
+  venue =        {Pittsburgh, PA},
+  month =        sep,
+  year =         {2023}
+}
+```
+
+We combined several multilingual ground truth datasets for misogyny and sexism (M/S) versus non-misogyny and non-sexism (non-M/S) [3,5,8,9,11,13, 20]. Specifically, the dataset expressing misogynistic or sexist speech (M/S) and the same number of texts expressing non-M/S speech in each language included 8, 582 English-language texts, 872 in French, 561 in Hindi, 2, 190 in Italian, and 612 in Bengali. The test data was a balanced set of 100 texts sampled randomly from both M/S and non-M/S groups in each language, for a total of 500 examples of M/S speech and 500 examples of non-M/S speech.
+
+References of the datasets are:
+
+3. Bhattacharya, S., et al.: Developing a multilingual annotated corpus of misog- yny and aggression, pp. 158–168. ELRA, Marseille, France, May 2020. https:// aclanthology.org/2020.trac- 1.25
+
+5. Chiril, P., Moriceau, V., Benamara, F., Mari, A., Origgi, G., Coulomb-Gully, M.: An annotated corpus for sexism detection in French tweets. In: Proceedings of LREC, pp. 1397–1403 (2020)
+
+8. Fersini, E., et al.: SemEval-2022 task 5: multimedia automatic misogyny identification. In: Proceedings of SemEval, pp. 533–549 (2022)
+
+9. Fersini, E., Nozza, D., Rosso, P.: Overview of the Evalita 2018 task on automatic misogyny identification (AMI). EVALITA Eval. NLP Speech Tools Italian 12, 59 (2018)
+
+11. Guest, E., Vidgen, B., Mittos, A., Sastry, N., Tyson, G., Margetts, H.: An expert annotated dataset for the detection of online misogyny. In: Proceedings of EACL, pp. 1336–1350 (2021)
+
+13. Jha, A., Mamidi, R.: When does a compliment become sexist? Analysis and classification of ambivalent sexism using Twitter data. In: Proceedings of NLP+CSS, pp. 7–16 (2017)
+
+20. Waseem, Z., Hovy, D.: Hateful symbols or hateful people? Predictive features for hate speech detection on Twitter. In: Proceedings of NAACL SRW, pp. 88–93 (2016)
+
+
+Please see the paper for more detail. 
+
 ---
 license: mit
 tags: