{"type":"ScholarlyArticle","title":"Detection and Analysis of Offensive Online Content in Hausa Language","authors":[{"name":"Fatima Muhammad Adam","slug":"fatima-muhammad-adam","given":"Fatima Muhammad","family":"Adam","affiliation":"Federal University Dutse , Nigeria"},{"name":"Abubakar Yakubu Zandam","slug":"abubakar-yakubu-zandam","given":"Abubakar Yakubu","family":"Zandam","affiliation":"Federal University of Technology Babura , Nigeria"},{"name":"Isa Inuwa-Dutse","slug":"isa-inuwa-dutse","given":"Isa","family":"Inuwa-Dutse","affiliation":"University of Huddersfield , UK"}],"abstract":"Hausa, a major Chadic language spoken by over 100 million people mostly in West Africa is considered a low-resource language from a computational linguistic perspective. This classification indicates a scarcity of linguistic resources and tools necessary for handling various natural language processing (NLP) tasks, including the detection of offensive content. To address this gap, we conducted two set of studies (1) a user study ($n=101$) to explore cyberbullying in Hausa and (2) an empirical study that led to the creation of the first dataset of offensive terms in the Hausa language. We developed detection systems trained on this dataset and compared their performance against relevant multilingual models, including Google Translate. Our detection system successfully identified over 70\\% of offensive, whereas baseline models frequently mistranslated such terms. We attribute this discrepancy to the nuanced nature of the Hausa language and the reliance of baseline models on direct or literal translation due to limited data to build purposive detection systems. These findings highlight the importance of incorporating cultural context and linguistic nuances when developing NLP models for low-resource languages such as Hausa. A post hoc analysis further revealed that offensive language is particularly prevalent in discussions related to religion and politics. To foster a safer online environment, we recommend involving diverse stakeholders with expertise in local contexts and demographics. Their insights will be crucial in developing more accurate detection systems and targeted moderation strategies that align with cultural sensitivities.","doi":"10.24963/ijcai.aai4g.2024/1","url":"https://ijcai.org/proceedings_aai4g/2024/1","pdf":"https://ijcai.org/proceedings_aai4g/2024/0001.pdf","markdown":"https://ijcai.org/proceedings_aai4g/2024/1.md","bibtex":"https://ijcai.org/proceedings_aai4g/2024/bibtex/1","volume":{"slug":"aai4g-2024","title":"Proceedings of the Second IJCAI AI for Good Symposium in Africa hosted by Deep Learning Indaba","short_title":"AI for Good Africa 2024","url":"https://ijcai.org/proceedings_aai4g/2024/","year":2024,"location":"Dakar, Senegal","dates":"1-7 September 2024","editors":"Avishkar Bhoopchand, Girmaw Abebe Tadesse, Sibusisiwe Makhanya, Frank Dignum, Georgina Curto","isbn":"978-1-956792-07-2","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.aai4g.2024"},"section":"Indaba","track":"Indaba","number":1,"pages":{"first":2,"last":14},"keywords":["Indaba: Indaba"],"videos":[],"references":[{"position":1,"raw":"[Abubakar et al., 2021] Amina Imam Abubakar, Abubakar Roko, Aminu Muhammad Bui, and Ibrahim Saidu. An enhanced feature acquisition for sentiment analysis of en- glish and hausa tweets. International Journal of Advanced Computer Science and Applications, 12(9), 2021.","title":"An enhanced feature acquisition for sentiment analysis of english and hausa tweets","authors":"Amina Imam Abubakar, Abubakar Roko, Aminu Muhammad Bui, Ibrahim Saidu","year":"2021","venue":"International Journal of Advanced Computer Science and Applications","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Al-Garadi et al., 2016] Mohammed Ali Al-Garadi, Kas- turi Dewi Varathan, and Sri Devi Ravana. Cybercrime de- tection in online communications: The experimental case of cyberbullying detection in the twitter network. Comput- ers in Human Behavior, 63:433-443, 2016.","title":"Cybercrime detection in online communications: The experimental case of cyberbullying detection in the twitter network","authors":"Mohammed Ali, Al-Garadi, Kasturi Dewi Varathan, Devi Sri, Ravana","year":"2016","venue":"Computers in Human Behavior","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Aliyu et al., 2023] Saminu Mohammad Aliyu, Idris Abdul- mumin, Shamsuddeen Hassan Muhammad, Ibrahim Said Ahmad, Saheed Abdullahi Salahudeen, Aliyu Yusuf, and Falalu Ibrahim Lawan. Hausanlp at semeval-2023 task 10: Transfer learning, synthetic data and side-information for multi-level sexism classification. arXiv preprint arXiv:2305.00076, 2023.","title":"Saminu Mohammad Aliyu, Idris Abdulmumin","authors":"Aliyu","year":"2023","venue":"Hausanlp at semeval-2023 task 10: Transfer learning, synthetic data and side-information for multi-level sexism classification","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Altay and Alatas, 2018] Elif Varol Altay and Bilal Alatas. Detection of cyberbullying in social networks using ma- chine learning methods. In 2018 International Congress on Big Data, Deep Learning and Fighting Cyber Terror- ism (IBIGDELFT), pages 87-91. IEEE, 2018.","title":"Detection of cyberbullying in social networks using machine learning methods","authors":"Alatas ; Altay, Varol Elif, Bilal Altay, Alatas","year":"2018","venue":"2018 International Congress on Big Data, Deep Learning and Fighting Cyber Terrorism (IBIGDELFT)","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Amin et al., 2020] Mohammad Amin, Seyed Mahdi Naha- vandi, and Mohammad Ghasemi. Various strategies for detecting online abusive content and combating cyber- bullying. IEEE Access, 8:64595-64606, 2020.","title":"Various strategies for detecting online abusive content and combating cyberbullying","authors":"Mohammad Amin, Seyed Mahdi Nahavandi, Mohammad Ghasemi","year":"2020","venue":"IEEE Access","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Antypas et al., 2022] Dimosthenis Antypas, Asahi Ushio, Jose Camacho-Collados, Leonardo Neves, Vítor Silva, and Francesco Barbieri. Twitter topic classification. arXiv preprint arXiv:2209.09824, 2022.","title":"Twitter topic classification","authors":"Dimosthenis Antypas, Asahi Ushio, Jose Camacho-Collados, Leonardo Neves, Vítor Silva, Francesco Barbieri","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Balakrishnan et al., 2023] Vimala Balakrishnan, Vithy- atheri Govindan, and Kumanan N Govaichelvan. Tamil offensive language detection: Supervised versus unsuper- vised learning approaches. ACM Transactions on Asian and Low-Resource Language Information Processing, 22(4):1-14, 2023.","title":"Tamil offensive language detection: Supervised versus unsupervised learning approaches","authors":"Vimala Balakrishnan, Vithyatheri Govindan, Kumanan N Govaichelvan","year":"2023","venue":"ACM Transactions on Asian and Low-Resource Language Information Processing","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Basile et al., 2019] Valerio Basile, Cristina Bosco, Elisa- betta Fersini, Debora Nozza, Viviana Patti, Francisco Manuel Rangel Pardo, Paolo Rosso, and Manuela San- guinetti. Semeval-2019 task 5: Multilingual detection of hate speech against immigrants and women in twitter. In Proceedings of the 13th international workshop on seman- tic evaluation, pages 54-63, 2019.","title":"Semeval-2019 task 5: Multilingual detection of hate speech against immigrants and women in twitter","authors":"Cristina Valerio Basile, Elisabetta Bosco, Debora Fersini, Viviana Nozza, Francisco Patti, Manuel Rangel, Paolo Pardo, Manuela Rosso, Sanguinetti","year":"2019","venue":"Proceedings of the 13th international workshop on semantic evaluation","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Bello et al., 2023] Bello Shehu Bello, Muham- mad Abubakar Alhassan, and Isa Inuwa-Dutse. # endsars protest: Discourse and mobilisation on twitter. arXiv preprint arXiv:2301.06176, 2023.","title":"# endsars protest: Discourse and mobilisation on twitter","authors":"Shehu Bello, Muhammad Bello, Isa Abubakar Alhassan, Inuwa-Dutse","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Bully Facts, 2023] Bully Facts. Bullying statistics. http://www.bullyingstatistics.org/category/bully-facts, 2023. Accessed: 13-09-2023.","title":"Bully Facts. Bullying statistics","authors":null,"year":"2023","venue":null,"doi":null,"url":"http://www.bullyingstatistics.org/category/bully-facts,2023","internal_url":null},{"position":11,"raw":"[Caselli et al., 2020a] Tommaso Caselli, Valerio Basile, Je- lena Mitrović, and Michael Granitzer. Hatebert: Retrain- ing bert for abusive language detection in english. arXiv preprint arXiv:2010.12472, 2020.","title":"Hatebert: Retraining bert for abusive language detection in english","authors":"Caselli","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Caselli et al., 2020b] Tommaso Caselli, Valerio Basile, Je- lena Mitrović, Inga Kartoziya, and Michael Granitzer. I feel offended, don't be abusive! implicit/explicit messages in offensive and abusive language. In Proceedings of the 12th language resources and evaluation conference, pages 6193-6202, 2020.","title":"I feel offended, don't be abusive! implicit/explicit messages in offensive and abusive language","authors":"Caselli","year":"2020","venue":"Proceedings of the 12th language resources and evaluation conference","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Chaure et al., 2019] Ms Mrunali D Chaure, Jayant P Mehare, et al. Text classification and analysis with social media platform. IJRAR-International Journal of Research and Analytical Reviews (IJRAR), 6(4):276-280, 2019.","title":"Text classification and analysis with social media platform","authors":"Ms Mrunali, D Chaure, Jayant P Mehare","year":"2019","venue":"IJRAR-International Journal of Research and Analytical Reviews (IJRAR)","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Conneau et al., 2019] Alexis Conneau, Kartikay Khandel- wal, Naman Goyal, Vishrav Chaudhary, Guillaume Wen- zek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer, and Veselin Stoyanov. Unsupervised cross- lingual representation learning at scale. arXiv preprint arXiv:1911.02116, 2019.","title":"Unsupervised crosslingual representation learning at scale","authors":"Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer, Veselin Stoyanov","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Dambo et al., 2022] Tamar Haruna Dambo, Metin Ersoy, Ahmad Muhammad Auwal, Victor Oluwafemi Olorun- sola, and Mehmet Bahri Saydam. Office of the citizen: a qualitative analysis of twitter activity during the lekki shooting in nigeria's# endsars protests. Information, Com- munication & Society, 25(15):2246-2263, 2022.","title":"Office of the citizen: a qualitative analysis of twitter activity during the lekki shooting in nigeria's# endsars protests","authors":"Dambo","year":"2022","venue":"Information, Communication & Society","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Davidson et al., 2019] Thomas Davidson, Debasmita Bhat- tacharya, and Ingmar Weber. Racial bias in hate speech and abusive language detection datasets. arXiv preprint arXiv:1905.12516, 2019.","title":"Racial bias in hate speech and abusive language detection datasets","authors":"Thomas Davidson, Debasmita Bhattacharya, Ingmar Weber","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Devlin et al., 2019] Jacob Devlin, Ming-Wei Chang, Ken- ton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understand- ing. In Proceedings of the 2019 conference of the North American chapter of the association for computational lin- guistics: human language technologies, volume 1 (long and short papers), pages 4171-4186, 2019.","title":"Bert: Pre-training of deep bidirectional transformers for language understanding","authors":"Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova","year":"2019","venue":"Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Dollarhide, 2021] M Dollarhide. Social media: Definition, effects, and list of top apps. Retrieved from Investopedia: https://www. investopedia. com/terms/s/social-media. asp, 2021.","title":"Social media: Definition, effects, and list of top apps","authors":"Dollarhide","year":null,"venue":null,"doi":null,"url":"https://www.investopedia.com/terms/s/social-media.asp,2021","internal_url":null},{"position":19,"raw":"[ECRI Policy, 2023] ECRI Policy. Euro- pean commission against racism and in- tolerance. https://www.coe.int/en/web/ european-commission-against-racism-and-intolerance/ recommendation-no.15, 2023. Accessed: 10-09-2023.","title":"ECRI Policy. European commission against racism and intolerance","authors":null,"year":"2023","venue":null,"doi":null,"url":"https://www.coe.int/en/web/european-commission-against-racism-and-intolerance/recommendation-no.15","internal_url":null},{"position":20,"raw":"[EU Code, 2023] EU Code. Code of conduct on coun- tering illegal hate speech online. https://ec.europa.eu/ commission/presscorner/detail/en/qanda 20 1135, 2023. Accessed: 15-09-2023.","title":"EU Code. Code of conduct on countering illegal hate speech","authors":null,"year":"2023","venue":null,"doi":null,"url":"https://ec.europa.eu/commission/presscorner/detail/en/qanda201135,2023","internal_url":null},{"position":21,"raw":"[Founta et al., 2018] Antigoni Maria Founta, Constantinos Djouvas, Despoina Chatzakou, Ilias Leontiadis, Jeremy Blackburn, Gianluca Stringhini, Athena Vakali, Michael Sirivianos, and Nicolas Kourtellis. Large scale crowd- sourcing and characterization of twitter abusive behavior. In Twelfth International AAAI Conference on Web and So- cial Media, 2018.","title":"Large scale crowdsourcing and characterization of twitter abusive behavior","authors":"Maria Antigoni, Constantinos Founta, Despoina Djouvas, Ilias Chatzakou, Jeremy Leontiadis, Gianluca Blackburn, Athena Stringhini, Michael Vakali, Nicolas Sirivianos, Kourtellis","year":"2018","venue":"Twelfth International AAAI Conference on Web and Social Media","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Freelon et al., 2016] Deen Freelon, Charlton D McIlwain, and Meredith Clark. Beyond the hashtags:# ferguson,# blacklivesmatter, and the online struggle for offline justice. Center for Media & Social Impact, American University, Forthcoming, 2016.","title":"Beyond the hashtags:# ferguson,# blacklivesmatter, and the online struggle for offline justice","authors":"Deen Freelon, Meredith Charlton D Mcilwain, Clark","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Haffner, 2018] Matthew Haffner. A spatial analysis of non- english twitter activity in houston, tx. Transactions in GIS, 22(4):913-929, 2018.","title":"A spatial analysis of nonenglish twitter activity in houston, tx","authors":"Matthew Haffner","year":"2018","venue":"Transactions in GIS","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Ibrahim et al., 2022] Umar Adam Ibrahim, Moussa Ma- hamat Boukar, and Muhammed Aliyu Suleiman. Devel- opment of hausa dataset a baseline for speech recognition. Data in Brief, 40:107820, 2022.","title":"Development of hausa dataset a baseline for speech recognition","authors":"Ibrahim","year":"2022","venue":"Data in Brief","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Inuwa-Dutse, 2021] Isa Inuwa-Dutse. The first large scale collection of diverse hausa language datasets. arXiv preprint arXiv:2102.06991, 2021.","title":"Isa Inuwa-Dutse. The first large scale collection of diverse hausa language datasets","authors":"Inuwa-Dutse","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":26,"raw":"Ishaku et al., ] Joy Nanyisopwa Ishaku, Muhammad Mustapha, and Ubaidallah Muhammad Bello. Contrastive analysis of lexical and structural ambiguity between hausa and english languages.","title":"Joy Nanyisopwa Ishaku, Muhammad Mustapha, and Ubaidallah Muhammad Bello. Contrastive analysis of lexical and structural ambiguity between hausa and english languages","authors":"Ishaku","year":null,"venue":null,"doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Kebriaei et al., 2023] Emad Kebriaei, Ali Homayouni, Roghayeh Faraji, Armita Razavi, Azadeh Shakery, Hes- haam Faili, and Yadollah Yaghoobzadeh. Persian offen- sive language detection. Machine Learning, pages 1-21, 2023.","title":"Persian offensive language detection","authors":"Emad Kebriaei, Ali Homayouni, Roghayeh Faraji, Armita Razavi, Azadeh Shakery, Heshaam Faili, Yadollah Yaghoobzadeh","year":"2023","venue":"Machine Learning","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Kennedy, 2006] Helen Kennedy. Beyond anonymity, or fu- ture directions for internet identity research. New media & society, 8(6):859-876, 2006.","title":"Beyond anonymity, or future directions for internet identity research","authors":"Helen Kennedy","year":"2006","venue":"New media & society","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Khan et al., 2023] Anas Ali Khan, M Hammad Iqbal, Shibli Nisar, Awais Ahmad, and Waseem Iqbal. Offensive lan- guage detection for low resource language using deep se- quence model. IEEE Transactions on Computational So- cial Systems, 2023.","title":"Offensive language detection for low resource language using deep sequence model","authors":"Anas Ali Khan, M Hammad Iqbal, Shibli Nisar, Awais Ahmad, Waseem Iqbal","year":"2023","venue":"IEEE Transactions on Computational Social Systems","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Kiela et al., 2020] Douwe Kiela, Hamed Firooz, Aravind Mohan, Vedanuj Goswami, Amanpreet Singh, Pratik Ringshia, and Davide Testuggine. The hateful memes challenge: Detecting hate speech in multimodal memes. Advances in neural information processing systems, 33:2611-2624, 2020.","title":"The hateful memes challenge: Detecting hate speech in multimodal memes","authors":"Douwe Kiela, Hamed Firooz, Aravind Mohan, Vedanuj Goswami, Amanpreet Singh, Pratik Ringshia, Davide Testuggine","year":"2020","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Kovács et al., 2022] György Kovács, Pedro Alonso, Rajku- mar Saini, and Marcus Liwicki. Leveraging external re- sources for offensive content detection in social media. AI Communications, 35(2):87-109, 2022.","title":"Leveraging external resources for offensive content detection in social media","authors":"György Kovács, Pedro Alonso, Rajkumar Saini, Marcus Liwicki","year":"2022","venue":"AI Communications","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Kumar et al., 2014] Shamanth Kumar, Fred Morstatter, and Huan Liu. Twitter data analytics. Springer, 2014.","title":"Twitter data analytics","authors":"Kumar","year":"2014","venue":null,"doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Kusumawardani and Basri, 2017] RP Kusumawardani and MH Basri. Topic identification and categorization of pub- lic information in community-based social media. In Jour- nal of Physics: Conference Series, volume 801, page 012075. IOP Publishing, 2017.","title":"RP Kusumawardani and MH Basri. Topic identification and categorization of public information in community-based social media","authors":"Basri Kusumawardani","year":"2017","venue":"Journal of Physics: Conference Series","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Machuve et al., 2022] Dina Machuve, Ciira Maina, and Wacuka Maina. Herdphobia: a dataset for hate speech against fulani in nigeria. 2022.","title":"Herdphobia: a dataset for hate speech against fulani in nigeria","authors":"Dina Machuve, Ciira Maina, Wacuka Maina","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Magee et al., 2013] Rachel M Magee, Denise E Agosto, Andrea Forte, June Ahn, Michael Dickard, and Rebecca Reynolds. Teens and social media: Where are we now, where next? Proceedings of the American Society for In- formation Science and Technology, 50(1):1-4, 2013.","title":"Teens and social media: Where are we now, where next","authors":"Denise E Rachel M Magee, Andrea Agosto, June Forte, Michael Ahn, Rebecca Dickard, Reynolds","year":"2013","venue":"Proceedings of the American Society for Information Science and Technology","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Markov et al., 2023] Todor Markov, Chong Zhang, Sand- hini Agarwal, Florentine Eloundou Nekoul, Theodore Lee, Steven Adler, Angela Jiang, and Lilian Weng. A holistic approach to undesired content detection in the real world. In Proceedings of the AAAI Conference on Artificial Intel- ligence, volume 37, pages 15009-15018, 2023.","title":"A holistic approach to undesired content detection in the real world","authors":"Todor Markov, Chong Zhang, Sandhini Agarwal, Florentine Eloundou Nekoul, Theodore Lee, Steven Adler, Angela Jiang, Lilian Weng","year":"2023","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Mathew et al., 2021] Binny Mathew, Punyajoy Saha, Seid Muhie Yimam, Chris Biemann, Pawan Goyal, and Animesh Mukherjee. Hatexplain: A benchmark dataset for explainable hate speech detection. In Proceedings of the AAAI conference on artificial intelligence, volume 35, pages 14867-14875, 2021.","title":"Hatexplain: A benchmark dataset for explainable hate speech detection","authors":"Binny Mathew, Punyajoy Saha, Seid Muhie Yimam, Chris Biemann, Pawan Goyal, Animesh Mukherjee","year":"2021","venue":"Proceedings of the AAAI conference on artificial intelligence","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Miao et al., 2023] Zhenxiong Miao, Xingshu Chen, Haizhou Wang, Rui Tang, Zhou Yang, Tiemai Huang, and Wenyi Tang. Detecting offensive language based on graph attention networks and fusion features. IEEE Transactions on Computational Social Systems, 2023.","title":"Detecting offensive language based on graph attention networks and fusion features","authors":"Miao","year":"2023","venue":"IEEE Transactions on Computational Social Systems","doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Mikolov et al., 2013] Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781, 2013.","title":"Efficient estimation of word representations in vector space","authors":"Tomas Mikolov, Kai Chen, Greg Corrado, Jeffrey Dean","year":"2013","venue":null,"doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Miller, 2010] Harvey J Miller. The data avalanche is here. shouldn't we be digging? Journal of Regional Science, 50(1):181-201, 2010.","title":"The data avalanche is here. shouldn't we be digging","authors":"J Harvey, Miller","year":"2010","venue":"Journal of Regional Science","doi":null,"url":null,"internal_url":null},{"position":41,"raw":"[Muhammad et al., 2022] Shamsuddeen Hassan Muham- mad, David Ifeoluwa Adelani, Ibrahim Said Ahmad, Idris Abdulmumin, Bello Shehu Bello, Monojit Choudhury, Chris Chinenye Emezue, Anuoluwapo Aremu, Saheed Abdul, and Pavel Brazdil. Naijasenti: A nigerian twit- ter sentiment corpus for multilingual sentiment analysis. arXiv preprint arXiv:2201.08277, 2022.","title":"Shamsuddeen Hassan Muhammad","authors":"Muhammad","year":"2022","venue":"Naijasenti: A nigerian twitter sentiment corpus for multilingual sentiment analysis","doi":null,"url":null,"internal_url":null},{"position":42,"raw":"[Nemes and Kiss, 2021] László Nemes and Attila Kiss. In- formation extraction and named entity recognition sup- ported social media sentiment analysis during the covid-19 pandemic. Applied Sciences, 11(22):11017, 2021.","title":"Information extraction and named entity recognition supported social media sentiment analysis during the covid-19 pandemic","authors":"Kiss Nemes, László Nemes, Attila Kiss","year":"2021","venue":"Applied Sciences","doi":null,"url":null,"internal_url":null},{"position":43,"raw":"[Nie et al., 2020] Yuyang Nie, Yuanhe Tian, Xiang Wan, Yan Song, and Bo Dai. Named entity recognition for social media texts with semantic augmentation. arXiv preprint arXiv:2010.15458, 2020.","title":"Named entity recognition for social media texts with semantic augmentation","authors":"Yuyang Nie, Yuanhe Tian, Xiang Wan, Yan Song, Bo Dai","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":44,"raw":"[Olteanu et al., 2015] Alexandra Olteanu, Ingmar Weber, and Daniel Gatica-Perez. Characterizing the demograph- ics behind the# blacklivesmatter movement. arXiv preprint arXiv:1512.05671, 2015.","title":"Characterizing the demographics behind the# blacklivesmatter movement","authors":"Alexandra Olteanu, Ingmar Weber, Daniel Gatica-Perez","year":"2015","venue":null,"doi":null,"url":null,"internal_url":null},{"position":45,"raw":"[Oriola and Kotzé, 2020] Oluwafemi Oriola and Eduan Kotzé. Evaluating machine learning techniques for de- tecting offensive and hate speech in south african tweets. IEEE Access, 8:21496-21509, 2020.","title":"Evaluating machine learning techniques for detecting offensive and hate speech in south african tweets","authors":"Kotzé Oriola, Oluwafemi Oriola, Eduan Kotzé","year":"2020","venue":"IEEE Access","doi":null,"url":null,"internal_url":null},{"position":46,"raw":"[Oyewusi et al., 2021] Wuraola Fisayo Oyewusi, Olubayo Adekanmbi, Ifeoma Okoh, Vitus Onuigwe, Mary Idera Salami, Opeyemi Osakuade, Sharon Ibejih, and Us- man Abdullahi Musa. Naijaner: Comprehensive named entity recognition for 5 nigerian languages. arXiv preprint arXiv:2105.00810, 2021.","title":"Wuraola Fisayo Oyewusi, Olubayo Adekanmbi","authors":"Oyewusi","year":"2021","venue":"Naijaner: Comprehensive named entity recognition for 5 nigerian languages","doi":null,"url":null,"internal_url":null},{"position":47,"raw":"[Plaza-Del-Arco et al., 2021] Flor Miriam Plaza-Del-Arco, M Dolores Molina-González, L Alfonso Ureña-López, and María Teresa Martín-Valdivia. A multi-task learn- ing approach to hate speech detection leveraging sentiment analysis. IEEE Access, 9:112478-112489, 2021.","title":"A multi-task learning approach to hate speech detection leveraging sentiment analysis","authors":"Flor Miriam, Plaza- Del-Arco, Dolores Molina-González, L Alfonso Ureña-López, María Teresa Martín-Valdivia","year":"2021","venue":"IEEE Access","doi":null,"url":null,"internal_url":null},{"position":48,"raw":"[Puspitasari, 2017] Septy Puspitasari. Arab spring: A case study of egyptian revolution 2011. Andalas Journal of In- ternational Studies (AJIS), 6(2):159-176, 2017.","title":"Septy Puspitasari. Arab spring: A case study of egyptian revolution 2011","authors":"Puspitasari","year":"2017","venue":"Andalas Journal of International Studies (AJIS)","doi":null,"url":null,"internal_url":null},{"position":49,"raw":"[Rafiq et al., 2018] Rahat Ibn Rafiq, Homa Hosseinmardi, Richard Han, Qin Lv, and Shivakant Mishra. Scalable and timely detection of cyberbullying in online social net- works. In Proceedings of the 33rd Annual ACM Sympo- sium on Applied Computing, pages 1738-1747, 2018.","title":"Scalable and timely detection of cyberbullying in online social networks","authors":"Rafiq","year":"2018","venue":"Proceedings of the 33rd Annual ACM Symposium on Applied Computing","doi":null,"url":null,"internal_url":null},{"position":50,"raw":"[Rajalakshmi et al., 2023] Ratnavel Rajalakshmi, Srivarshan Selvaraj, Pavitra Vasudevan, et al. Hottest: Hate and of- fensive content identification in tamil using transformers and enhanced stemming. Computer Speech & Language, 78:101464, 2023.","title":"Hottest: Hate and offensive content identification in tamil using transformers and enhanced stemming","authors":"Rajalakshmi","year":"2023","venue":"Computer Speech & Language","doi":null,"url":null,"internal_url":null},{"position":51,"raw":"[Rane and Salem, 2012] Halim Rane and Sumra Salem. So- cial media, social movements and the diffusion of ideas in the arab uprisings. Journal of international communica- tion, 18(1):97-111, 2012.","title":"Social media, social movements and the diffusion of ideas in the arab uprisings","authors":"Halim Rane, Sumra Salem","year":"2012","venue":"Journal of international communication","doi":null,"url":null,"internal_url":null},{"position":52,"raw":"[Saeed et al., 2023] Ramsha Saeed, Hammad Afzal, Sadaf Abdul Rauf, and Naima Iltaf. Detection of offensive language and its severity for low resource language. ACM Transactions on Asian and Low-Resource Language Information Processing, 22(6):1-27, 2023.","title":"Detection of offensive language and its severity for low resource language","authors":"Saeed","year":"2023","venue":"ACM Transactions on Asian and Low-Resource Language Information Processing","doi":null,"url":null,"internal_url":null},{"position":53,"raw":"[Sanguinetti et al., 2018] Manuela Sanguinetti, Fabio Po- letto, Cristina Bosco, Viviana Patti, and Marco Stranisci. An italian twitter corpus of hate speech against immi- grants. In Proceedings of the eleventh international con- ference on language resources and evaluation (LREC 2018), 2018.","title":"An italian twitter corpus of hate speech against immigrants","authors":"Manuela Sanguinetti, Fabio Poletto, Cristina Bosco, Viviana Patti, Marco Stranisci","year":"2018","venue":"Proceedings of the eleventh international conference on language resources and evaluation (LREC 2018)","doi":null,"url":null,"internal_url":null},{"position":54,"raw":"Sani et al., ] Muhammad Sani, Abubakar Ahmad, and Hadiza S Abdulazeez. Sentiment analysis of hausa lan- guage tweet using machine learning approach. [Schmidt and Wiegand, 2017]","title":"Sentiment analysis of hausa language tweet using machine learning approach","authors":"Sani","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":55,"raw":"Anna Schmidt and Michael Wiegand. A survey on hate speech detection using natural language processing. In Proceedings of the fifth interna- tional workshop on natural language processing for social media, pages 1-10, 2017.","title":"A survey on hate speech detection using natural language processing","authors":"Anna Schmidt, Michael Wiegand","year":"2017","venue":"Proceedings of the fifth international workshop on natural language processing for social media","doi":null,"url":null,"internal_url":null},{"position":56,"raw":"[Simpson. and Weiner, 2019] John Simpson. and Edmund Weiner. Oxford of Dictionary. Oxford University Press, Oxford, UK, 2019.","title":null,"authors":"Simpson, Weiner","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":57,"raw":"[Sinyangwe et al., 2023] Clement Sinyangwe, Douglas Kunda, and William Phiri Abwino. Detecting hate speech and offensive language using machine learning in pub- lished online content. Zambia ICT Journal, 7(1):79-84, 2023.","title":"Detecting hate speech and offensive language using machine learning in published online content","authors":"Clement Sinyangwe, Douglas Kunda, William Phiri, Abwino","year":"2023","venue":"Zambia ICT Journal","doi":null,"url":null,"internal_url":null},{"position":58,"raw":"[Suleiman et al., 2019] MA Suleiman, Muktar M Aliyu, and SI Zimit. Towards the development of hausa language cor- pus. Int. J. Sci. Eng. Res, 10:1598-1604, 2019.","title":"Towards the development of hausa language corpus","authors":"Suleiman","year":"2019","venue":"Int. J. Sci. Eng. Res","doi":null,"url":null,"internal_url":null},{"position":59,"raw":"[Tsvetkov, 2017] Yulia Tsvetkov. Opportunities and chal- lenges in working with low-resource languages. Slides Part-1, 2017.","title":"Opportunities and challenges in working with low-resource languages","authors":"Yulia Tsvetkov","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":60,"raw":"[Van Hee et al., 2018] Cynthia Van Hee, Gilles Jacobs, Chris Emmery, Bart Desmet, Els Lefever, Ben Verhoeven, Guy De Pauw, Walter Daelemans, and Véronique Hoste. Auto- matic detection of cyberbullying in social media text. PloS one, 13(10):e0203794, 2018.","title":"Automatic detection of cyberbullying in social media text","authors":"Cynthia Van Hee, Gilles Jacobs, Chris Emmery, Bart Desmet, Els Lefever, Ben Verhoeven, Guy De Pauw, Walter Daelemans, Véronique Hoste","year":"2018","venue":"PloS one","doi":null,"url":null,"internal_url":null},{"position":61,"raw":"[Warf, 2018] Barney Warf. The SAGE Encyclopedia of the Internet. Sage, 2018.","title":"The SAGE Encyclopedia of the Internet","authors":"Barney Warf, Warf","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":62,"raw":"[Yao et al., 2019] Mengfan Yao, Charalampos Chelmis, and Daphney-Stavroula Zois. Cyberbullying ends here: To- wards robust detection of cyberbullying in social media. In The World Wide Web Conference, pages 3427-3433, 2019.","title":"Cyberbullying ends here: Towards robust detection of cyberbullying in social media","authors":"Yao","year":"2019","venue":"The World Wide Web Conference","doi":null,"url":null,"internal_url":null},{"position":63,"raw":"[Zandam et al., 2023] Abubakar Yakubu Zandam, Fa- tima Adam Muhammad, and Isa Inuwa-Dutse. Online threats detection in hausa language. In 4th Workshop on African Natural Language Processing, 2023.","title":"Online threats detection in hausa language","authors":"Abubakar Yakubu Zandam, Fatima Adam Muhammad, Isa Inuwa-Dutse","year":"2023","venue":"4th Workshop on African Natural Language Processing","doi":null,"url":null,"internal_url":null},{"position":64,"raw":"[Zhong et al., 2016] Haoti Zhong, Hao Li, Anna Cinzia Squicciarini, Sarah Michele Rajtmajer, Christopher Grif- fin, David J Miller, and Cornelia Caragea. Content-driven detection of cyberbullying on the instagram social net- work. In IJCAI, volume 16, pages 3952-3958, 2016.","title":"Content-driven detection of cyberbullying on the instagram social network","authors":"Haoti Zhong, Hao Li, Anna Cinzia Squicciarini, Sarah Michele Rajtmajer, Christopher Griffin, David J Miller, Cornelia Caragea","year":"2016","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2016/556"},{"position":65,"raw":"[Zishumba, 2019] Kudzai Zishumba. Sentiment Analysis Based on Social Media Data. PhD thesis, 2019.","title":"Sentiment Analysis Based on Social Media Data","authors":"Kudzai Zishumba","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":66,"raw":"[Zois et al., 2018] Daphney-Stavroula Zois, Angeliki Kapodistria, Mengfan Yao, and Charalampos Chelmis. Optimal online cyberbullying detection. In 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 2017-2021. IEEE, 2018.","title":"Optimal online cyberbullying detection","authors":"Daphney-Stavroula Zois, Angeliki Kapodistria, Mengfan Yao, Charalampos Chelmis","year":"2018","venue":"2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}