Gebremichael, Hagos Tesfahun
AfriHate: A Multilingual Collection of Hate Speech and Abusive Language Datasets for African Languages
Muhammad, Shamsuddeen Hassan, Abdulmumin, Idris, Ayele, Abinew Ali, Adelani, David Ifeoluwa, Ahmad, Ibrahim Said, Aliyu, Saminu Mohammad, Onyango, Nelson Odhiambo, Wanzare, Lilian D. A., Rutunda, Samuel, Aliyu, Lukman Jibril, Alemneh, Esubalew, Hourrane, Oumaima, Gebremichael, Hagos Tesfahun, Ismail, Elyas Abdi, Beloucif, Meriem, Jibril, Ebrahim Chekol, Bukula, Andiswa, Mabuya, Rooweither, Osei, Salomey, Oppong, Abigail, Belay, Tadesse Destaw, Guge, Tadesse Kebede, Asfaw, Tesfa Tegegne, Chukwuneke, Chiamaka Ijeoma, Röttger, Paul, Yimam, Seid Muhie, Ousidhoum, Nedjma
Hate speech and abusive language are global phenomena that need socio-cultural background knowledge to be understood, identified, and moderated. However, in many regions of the Global South, there have been several documented occurrences of (1) absence of moderation and (2) censorship due to the reliance on keyword spotting out of context. Further, high-profile individuals have frequently been at the center of the moderation process, while large and targeted hate speech campaigns against minorities have been overlooked. These limitations are mainly due to the lack of high-quality data in the local languages and the failure to include local communities in the collection, annotation, and moderation processes. To address this issue, we present AfriHate: a multilingual collection of hate speech and abusive language datasets in 15 African languages. Each instance in AfriHate is annotated by native speakers familiar with the local culture. We report the challenges related to the construction of the datasets and present various classification baseline results with and without using LLMs. The datasets, individual annotations, and hate speech and offensive language lexicons are available on https://github.com/AfriHate/AfriHate
AfriSenti: A Twitter Sentiment Analysis Benchmark for African Languages
Muhammad, Shamsuddeen Hassan, Abdulmumin, Idris, Ayele, Abinew Ali, Ousidhoum, Nedjma, Adelani, David Ifeoluwa, Yimam, Seid Muhie, Ahmad, Ibrahim Sa'id, Beloucif, Meriem, Mohammad, Saif M., Ruder, Sebastian, Hourrane, Oumaima, Brazdil, Pavel, Ali, Felermino Dário Mário António, David, Davis, Osei, Salomey, Bello, Bello Shehu, Ibrahim, Falalu, Gwadabe, Tajuddeen, Rutunda, Samuel, Belay, Tadesse, Messelle, Wendimu Baye, Balcha, Hailu Beshada, Chala, Sisay Adugna, Gebremichael, Hagos Tesfahun, Opoku, Bernard, Arthur, Steven
Africa is home to over 2,000 languages from more than six language families and has the highest linguistic diversity among all continents. These include 75 languages with at least one million speakers each. Yet, there is little NLP research conducted on African languages. Crucial to enabling such research is the availability of high-quality annotated datasets. In this paper, we introduce AfriSenti, a sentiment analysis benchmark that contains a total of >110,000 tweets in 14 African languages (Amharic, Algerian Arabic, Hausa, Igbo, Kinyarwanda, Moroccan Arabic, Mozambican Portuguese, Nigerian Pidgin, Oromo, Swahili, Tigrinya, Twi, Xitsonga, and Yor\`ub\'a) from four language families. The tweets were annotated by native speakers and used in the AfriSenti-SemEval shared task (The AfriSenti Shared Task had over 200 participants. See website at https://afrisenti-semeval.github.io). We describe the data collection methodology, annotation process, and the challenges we dealt with when curating each dataset. We further report baseline experiments conducted on the different datasets and discuss their usefulness.