Table of Contents

• Preface [doi]
• Overview of the HASOC Subtrack at FIRE 2021: HateSpeech and Offensive Content Identification in English and Indo-Aryan LanguagesThomas Mandl 0001, Sandip Modha, Gautam Kishore Shahi, Hiren Madhu, Shrey Satapara, Prasenjit Majumder, Johannes Schäfer, Tharindu Ranasinghe, Marcos Zampieri, Durgesh Nandini, Amit Kumar Jaiswal. 1-19 [doi]
• Overview of the HASOC Subtrack at FIRE 2021: Conversational Hate Speech Detection in Code-mixed languageShrey Satapara, Sandip Modha, Thomas Mandl 0001, Hiren Madhu, Prasenjit Majumder. 20-31 [doi]
• Exploring Transformer Based Models to Identify Hate Speech and Offensive Content in English and Indo-Aryan LanguagesSomnath Banerjee 0002, Maulindu Sarkar, Nancy Agrawal, Punyajoy Saha, Mithun Das. 32-43 [doi]
• Hate Speech and Offensive Content Identification with Graph Convolutional NetworksNecva Bölücü, Pelin Canbay. 44-51 [doi]
• Fine-tuning of Pre-trained Transformers for Hate Offensive and Profane Content Detection in English and MarathiAnna Glazkova, Michael Kadantsev, Maksim Glazkov. 52-62 [doi]
• Leveraging Transformers for Hate Speech Detection in Conversational Code-Mixed TweetsZaki Mustafa Farooqi, Sreyan Ghosh, Rajiv Ratn Shah. 63-74 [doi]
• Detecting Hate Speech on English and Indo-Aryan Languages with BERT and Ensemble learningCamilo Caparrós-Laiz, José Antonio García-Díaz, Rafael Valencia-García. 75-81 [doi]
• A Simple Language-Agnostic yet Strong Baseline System for Hate Speech and Offensive Content IdentificationYves Bestgen. 82-91 [doi]
• Multilingual Hate Speech and Offensive Language Detection in English Hindi and Marathi languagesKalaivani Adaikkan, Thenmozhi Durairaj. 92-103 [doi]
• Hate Speech and Offensive Content Identification in English TweetsRitesh Kumar, Vishesh Gupta, Rajendra Pamula. 104-109 [doi]
• Machine Learning Models for Hate Speech and Offensive Language Identification for Indo-Aryan Language: HindiPurva Mankar, Akshaya Gangurde, Deptii Chaudhari, Ambika Pawar. 110-120 [doi]
• Probabilistic Impact Score Generation using Ktrain-BERT to Identify Hate Words from Twitter DiscussionsSourav Das, Prasanta Mandal, Sanjay Chatterji. 121-131 [doi]
• Ensemble Based Machine Learning Models for Hate Speech and Offensive Content IdentificationAsha Hegde, Mudoor Devadas Anusha, Hosahalli Lakshmaiah Shashirekha. 132-141 [doi]
• Classification of Hate Speech and Offensive Content using an approach based on DistilBERTSwetha Saseendran, Sudharshan R, Sreedhar V, Sharan Giri. 142-153 [doi]
• Hate Speech and Offensive Content Identification Based on Self-AttentionYifan Xu, Hui Ning, Yutong Sun. 154-160 [doi]
• Battling Hateful Content in Indic Languages HASOC'21Aditya Kadam, Anmol Goel, Jivitesh Jain, Jushaan Singh Kalra, Mallika Subramanian, Manvith Reddy, Prashant Kodali, T. H. Arjun, Manish Shrivastava 0001, Ponnurangam Kumaraguru. 161-172 [doi]
• A Feature Extraction Based Model for Hate Speech IdentificationSalar Mohtaj, Vera Schmitt, Sebastian Möller 0001. 173-181 [doi]
• Attention Based BERT-FastText Model for Hate Speech and Offensive Content Identification in English and Hindi LanguagesKrishanu Maity, Abhishek Kumar, Sriparna Saha 0001. 182-190 [doi]
• Detect Hate and Offensive Content in English and Indo-Aryan Languages based on TransformerYongyi Kui. 191-199 [doi]
• Applying Transfer Learning using BERT-Based Models for Hate Speech DetectionSakshi Kalra, Kalit Naresh Inani, Yashvardhan Sharma, Gajendra Singh Chauhan. 200-208 [doi]
• Hatespeech and Offensive Content Detection in Hindi Language using C-BiGRUSudharsana Kannan, Jelena Mitrovic. 209-216 [doi]
• Contextual Hate Speech Detection in Code Mixed Text using Transformer Based ApproachesRavindra Nayak, Raviraj Joshi. 217-225 [doi]
• Offensive Language Identification Using Hindi-English Code-Mixed Tweets and Code-Mixed Data AugmentationMd Saroar Jahan, Mourad Oussalah 0002, Jhuma Kabir Mim, Mominul Islam. 226-238 [doi]
• Hate and Offensive Speech Detection in Hindi and MarathiAbhishek Velankar, Hrushikesh Patil, Amol Gore, Shubham Salunke, Raviraj Joshi. 239-247 [doi]
• Leveraging Text Generated from Emojis for Hate Speech and Offensive Content IdentificationNkwebi Peace Motlogelwa, Edwin Thuma, Monkgogi Mudongo, Tebo Leburu-Dingalo, Gontlafetse Mosweunyane. 248-253 [doi]
• ALBERT for Hate Speech and Offensive Content IdentificationJun Zeng, Li Xu, Hao Wu. 254-261 [doi]
• Hate and Offensive Language Detection using BERT for English Subtask AMd Saroar Jahan, Djamila Romaissa Beddiar, Mourad Oussalah 0002, Nabil Arhab, Yazid Bounab. 262-272 [doi]
• Transformer Models for Offensive Language Identification in MarathiMayuresh Nene, Kai North, Tharindu Ranasinghe, Marcos Zampieri. 273-282 [doi]
• Offensive Text Detection on English Twitter with Deep Learning Models and Rule-Based SystemsKinga Gémes, Ádám Kovács, Markus Reichel, Gábor Recski. 283-296 [doi]
• Multi-Task Learning with Sentiment Emotion and Target Detection to Recognize Hate Speech and Offensive LanguageFlor Miriam Plaza del Arco, Sercan Halat, Sebastian Padó, Roman Klinger. 297-318 [doi]
• Hybrid Representation Fusion for Twitter Hate Speech IdentificationWentao Yu, Benedikt T. Boenninghoff, Dorothea Kolossa. 319-329 [doi]
• Fine-tune BERT to Classify Hate Speech in Hindi English Code-Mixed TextShikha Mundra, Nikhil Singh, Namita Mittal. 330-337 [doi]
• Hate and Offensive Speech Detection in Hindi Twitter CorpusIshali Jadhav, Aditi Kanade, Vishesh Waghmare, Deptii Chaudhari. 338-348 [doi]
• Multilingual Hate Speech and Offensive Content Detection using Modified Cross-entropy LossArka Mitra, Priyanshu Sankhala. 349-356 [doi]
• biCourage: ngram and syntax GCNs for Hate Speech detectionRodrigo Souza Wilkens, Dimitri Ognibene. 357-366 [doi]
• Detection of Hate or Offensive Phrase using Magnified Tf-IdfPalash Nandi, Dipankar Das. 367-378 [doi]
• Machine Learning Models for Hate Speech Identification in Marathi LanguageDisha Gajbhiye, Swapnil Deshpande, Prerna Ghante, Abhijeet Kale, Deptii Chaudhari. 379-386 [doi]
• Gated Multi-task learning framework for text classificationSuyash Sangwan, Lipika Dey, Mohammad Shakir. 387-395 [doi]
• Hate Speech Detection using LIME guided Ensemble Method and DistilBERTDeepakindresh N, Rohan Avireddy, Aakash Ambalavanan, B. Radhika Selvamani. 396-411 [doi]
• Combining Textual Features for the Detection of Hateful and Offensive LanguageSherzod Hakimov, Ralph Ewerth. 412-418 [doi]
• One to Rule Them All: Towards Joint Indic Language Hate Speech DetectionMehar Bhatia, Tenzin Singhay Bhotia, Akshat Agarwal, Prakash Ramesh, Shubham Gupta, Kumar Shridhar, Felix Laumann, Ayushman Dash. 419-431 [doi]
• Machine Learning Based Hate Speech Identification for English and Indo-Aryan LanguagesAnirudh Anand, Jeet Golecha, Bharathi B, Bhuvana Jayaraman, Mirnalinee T. T. 432-438 [doi]
• An Ensemble Approach for Hate and Offensive Language Identification in English and Indo-Aryan LanguagesAbhinav Kumar, Pradeep Kumar Roy, Sunil Saumya. 439-445 [doi]
• Fine-tuning Pre-Trained Transformer based model for Hate Speech and Offensive Content Identification in English Indo-Aryan and Code-Mixed (English-Hindi) languagesSupriya Chanda, S. Ujjwal, Shayak Das, Sukomal Pal. 446-458 [doi]
• SVM for Hate Speech and Offensive Content DetectionShyam Ratan, Sonal Sinha, Siddharth Singh. 459-466 [doi]
• Hate Speech and Offensive Content Identification in Hindi and Marathi Language Tweets using Ensemble TechniquesRatnavel Rajalakshmi, Faerie Mattins, Srivarshan S, Preethi Reddy, M. Anand Kumar. 467-479 [doi]
• Conversational Hate-Speech detection in Code-Mixed Hindi-English TweetsRatnavel Rajalakshmi, Srivarshan S, Faerie Mattins, Kaarthik E, Prithvi Seshadri, M. Anand Kumar. 480-490 [doi]
• Classification of Hate Offensive and Profane content from Tweets using an Ensemble of Deep Contextualized and Domain Specific RepresentationsBasavraj Chinagundi, Muskaan Singh, Tirthankar Ghosal, Prashant Singh Rana, Guneet Singh Kohli. 491-500 [doi]
• Detecting Offensive Language in English Hindi and Marathi using Classical Supervised Machine Learning Methods and Word/Char N-gramsYaakov HaCohen-Kerner, Moshe Uzan. 501-507 [doi]
• Feature Selection with Pretrained-BERT for Hate Speech and Offensive Content Identification in English and Hindi LanguagesSurya Agustian, Reski Saputra, Aidil Fadhilah. 508-516 [doi]
• Overview of the Third Shared Task on Artificial Intelligence for Legal Assistance at FIRE 2021Vedant Parikh, Upal Bhattacharya, Parth Mehta 0001, Ayan Bandyopadhyay, Paheli Bhattacharya, Kripabandhu Ghosh, Saptarshi Ghosh 0001, Arindam Pal 0001, Arnab Bhattacharya 0001, Prasenjit Majumder. 517-526 [doi]
• Categorizing Roles of Legal Texts via Sequence Tagging on Domain-Specific Language ModelsSourav Dutta. 527-533 [doi]
• DistilRoBERTa Based Sentence Embedding for Rhetorical Role Labelling of Legal Case DocumentsDeepthi Sudharsan, Asmitha U, Premjith B, Soman K. P. 534-540 [doi]
• Legal Text Classification and Summarization using Transformers and Joint Text FeaturesShaz Furniturewala, Racchit Jain, Vijay Kumari, Yashvardhan Sharma. 541-546 [doi]
• Classification on Sentence Embeddings for LegalAssistanceArka Mitra. 547-552 [doi]
• Summarization of Indian Legal Judgement Documents via Ensembling of Contextual Embedding based MLP ModelsDeepali Jain, Malaya Dutta Borah, Anupam Biswas. 553-561 [doi]
• Simple Transformers in Rhetoric Role Labelling for Legal JudgementsSai Shridhar Balamurali, Kayalvizhi S, Thenmozhi D. 562-567 [doi]
• Rhetorical Role Labelling for Legal Judgements and Legal Document SummarizationSiddhartha Rusiya, Aditya Sharma, Debajyoti Debbarma, Samarjit Debbarma. 568-574 [doi]
• Rhetorical Role Labelling for Legal Judgements using fastText ClassifierTebo Leburu-Dingalo, Edwin Thuma, Gontlafetse Mosweunyane, Nkwebi Peace Motlogelwa. 575-580 [doi]
• Automatic Detection of Rhetorical Role Labels using ERNIE2.0 and RoBERTaGuneet Singh Kohli, Prabsimran Kaur, Jatin Bedi. 581-588 [doi]
• Overview of the HASOC-DravidianCodeMix Shared Task on Offensive Language Detection in Tamil and MalayalamBharathi Raja Chakravarthi, Prasanna Kumar Kumaresan, Ratnasingam Sakuntharaj, Anand Kumar Madasamy, Sajeetha Thavareesan, B. Premjith, Sreelakshmi K, Subalalitha Chinnaudayar Navaneethakrishnan, John P. McCrae, Thomas Mandl 0001. 589-602 [doi]
• CoMaTa OLI- Code-Mixed Malayalam and Tamil Offensive Language IdentificationFazlourrahman Balouchzahi, S. Bashang, Grigori Sidorov, H. L. Shashirekha. 603-614 [doi]
• Hate Speech and Offensive Language Identification on Multilingual Code Mixed Text using BERTSnehaan Bhawal, Pradeep Roy, Abhinav Kumar. 615-624 [doi]
• Analyzing Social Media Content for Detection of Offensive TextPawan Kalyan Jada, Konthala Yasaswini, Karthik Puranik, Anbukkarasi Sampath, Sathiyaraj Thangasamy, Kingston Pal Thamburaj. 625-635 [doi]
• Hate and Offensive Content Identification from Dravidian Social Media Posts: A Deep Learning ApproachAnu Priya, Abhinav Kumar. 636-642 [doi]
• Offensive Language Identification on Multilingual Code Mixing TextJyoti Kumari, Abhinav Kumar. 643-650 [doi]
• Transformer Based Model For Offensive Content Recognition In Dravidian LanguagesDivya S, Sripriya N. 651-658 [doi]
• Pretrained Transformers for Offensive Language Identification in TanglishSean Benhur, Kanchana Sivanraju. 659-666 [doi]
• TOLD: Tamil Offensive Language Detection in Code-Mixed Social Media Comments using MBERT with Features based SelectionKalaivani Adaikkan, Thenmozhi Durairaj, Aravindan Chandrabose. 667-679 [doi]
• mBERT Based Model for Identification of Offensive Content in South Indian LanguagesShankar Biradar, Sunil Saumya, Arun Chauhan 0003. 680-687 [doi]
• Offensive Text Prediction using Machine Learning and Deep Learning ApproachesBhuvana Jayaraman, Mirnalinee T. T, Karthik Raja Anandan, Aarthi Suresh Kumar, Anirudh Anand. 688-695 [doi]
• Transformer Ensemble System for Detection of Offensive Content in Dravidian LanguagesB. S. N. V. Chaitanya, Karri Anjali. 696-704 [doi]
• Offensive Language Identification using Machine Learning and Deep Learning TechniquesJerin Mahibha C, Kayalvizhi Sampath, Durairaj Thenmozhi, Arunima S.. 705-713 [doi]
• Offensive Language Classification of Code-Mixed Tamil with KerasSuchismita Tripathy, Ameya Pathak, Yashvardhan Sharma. 714-719 [doi]
• Detection Offensive Tamil Texts using Machine Learning and Multilingual Transformers ModelsMalliga Subramanian, Shanmuga Vadivel Kogilavani, Antonette Shibani, Adhithiya G. J, Deepti Ravi, Gowthamkrishnan S. 720-728 [doi]
• IndicBERT Based Approach for Sentiment Analysis on Code-Mixed Tamil TweetsR. Ramesh Kannan, Ratnavel Rajalakshmi, Lokesh Kumar. 729-736 [doi]
• RNN's VS TRANSFORMERS: Training Language Models on Deficit DatasetsAbhishek Kumar Gautam, Bharathi B. 737-743 [doi]
• Overview of Abusive and Threatening Language Detection in Urdu at FIRE 2021Maaz Amjad, Alisa Zhila, Grigori Sidorov, Andrey Labunets, Sabur Butt, Hamza Imam Amjad, Oxana Vitman, Alexander F. Gelbukh. 744-762 [doi]
• Abusive and Threatening Language Detection in Urdu using Supervised Machine Learning and Feature CombinationsMuhammad Humayoun. 763-773 [doi]
• Urdu Abusive Language Detection using Machine LearningMuhammad Owais Raza, Qaisar Khan, Ghulam Muhammad Soomro. 774-783 [doi]
• Abusive and Threatening Language Detection from Urdu Social Media Posts: A machine learning approachAbhinav Kumar, Sunil Saumya, Pradeep Kumar Roy. 784-790 [doi]
• Abusive and Threatening Language Detection in Urdu using Boosting Based and BERT Based Models: A Comparative ApproachMithun Das, Somnath Banerjee 0002, Punyajoy Saha. 791-798 [doi]
• Detection of Abusive Records by Analyzing the Tweets in Urdu Language Exploring Transformer Based ModelsSakshi Kalra, Yash Bansal, Yashvardhan Sharma. 799-805 [doi]
• Abusive and Threatening Language Detection in Native Urdu Script Tweets Exploring Four Conventional Machine Learning Techniques and MLPK. A. Karthikraja, Aarthi Suresh Kumar, B. Bharathi, Jayaraman Bhuvana, T. T. Mirnalinee. 806-812 [doi]
• Detection of Threat Records by Analyzing the Tweets in Urdu Language Exploring Deep Learning Transformer - Based ModelsSakshi Kalra, Mehul Agrawal, Yashvardhan Sharma. 813-819 [doi]
• ArMI at FIRE 2021: Overview of the First Shared Task on Arabic Misogyny IdentificationHala Mulki, Bilal Ghanem. 820-830 [doi]
• A Deep Learning Approach for Identification of Arabic Misogyny from TweetsAbhinav Kumar, Pradeep Kumar Roy, Jyoti Prakash Singh. 831-838 [doi]
• Arabic Misogyny IdentificationFazlourrahman Balouchzahi, Grigori Sidorov, Hosahalli Lakshmaiah Shashirekha. 839-846 [doi]
• Context-aware Language Modeling for Arabic Misogyny IdentificationIstabrak Abbes, Eya Nakache, Moez BenHajhmida. 847-851 [doi]
• Deep Multi-Task Models for Misogyny Identification and Categorization on Arabic Social MediaAbdelkader El Mahdaouy, Abdellah El Mekki, Ahmed Oumar, Hajar Mousannif, Ismail Berrada. 852-860 [doi]
• Detecting Misogyny in Arabic TweetsAbdusalam Nwesri, Stephen Wu, Harmain Harmain. 861-866 [doi]
• iCompass Working Notes for Arabic Misogyny IdentificationAbir Messaoudi, Chayma Fourati, Mayssa Kchaou, Hatem Haddad. 867-871 [doi]
• Findings of the Sentiment Analysis of Dravidian Languages in Code-Mixed TextBharathi Raja Chakravarthi, Ruba Priyadharshini, Sajeetha Thavareesan, Dhivya Chinnappa, Durairaj Thenmozhi, Elizabeth Sherly, John P. McCrae, Adeep Hande, Rahul Ponnusamy, Shubhanker Banerjee, Charangan Vasantharajan. 872-886 [doi]
• CoSaD- Code-Mixed Sentiments Analysis for Dravidian LanguagesFazlourrahman Balouchzahi, Hosahalli Lakshmaiah Shashirekha, Grigori Sidorov. 887-898 [doi]
• Sentiment Classification on Bilingual Code-Mixed Texts for Dravidian Languages using Machine Learning MethodsRashmi K. B., Guruprasad H. S., Shambhavi B. R.. 899-907 [doi]
• Sentiment Analysis on Multilingual Code-Mixed Kannada LanguageSatyam Dutta, Himanshi Agrawal, Pradeep Kumar Roy. 908-918 [doi]
• Automatic Detecting the Sentiment of Code-Mixed Text by Pre-training ModelYang Bai, Bangyuan Zhang, Wanli Chen, Yongjie Gu, Tongfeng Guan, Qisong Shi. 919-925 [doi]
• Transformer based Sentiment Analysis in Dravidian LanguagesPawan Kalyan Jada, D. Sashidhar Reddy, Konthala Yasaswini, Arunaggiri Pandian K, Prabakaran Chandran, Anbukkarasi Sampath, Sathiyaraj Thangasamy. 926-938 [doi]
• Transliterate or Translate? Sentiment Analysis of Code-Mixed Text in Dravidian LanguagesKarthik Puranik, Bharathi B, Senthil Kumar B. 939-949 [doi]
• An Ensemble-Based Model for Sentiment Analysis of Dravidian Code-Mixed Social Media PostsAbhinav Kumar, Sunil Saumya, Jyoti Prakash Singh. 950-958 [doi]
• A Deep Neural Network-based Model for the Sentiment Analysis of Dravidian Code-mixed Social Media PostsJyoti Kumari, Abhinav Kumar. 959-966 [doi]
• ECMAG - Ensemble of CNN and Multi-Head Attention with Bi-GRU for Sentiment Analysis in Code-Mixed DataDhanasekaran Prasannakumaran, Jappeswaran Balasubramanian Sideshwar, Durairaj Thenmozhi. 967-975 [doi]
• Comparative Analysis for Offensive Language Identification of Tamil Text using SVM and Logistic ClassifierPrabhu Ram. N, Meeradevi. T, Vibin Mammen Vinod, Gothainayaki. A, Anusha S, Agalya T. 976-983 [doi]
• Sentiment Analysis on Code-mixed Dravidian Languages A Non-Linguistic ApproachPrasad A. Joshi, Varsha M. Pathak. 984-995 [doi]
• BiLSTM-Sentiments Analysis in Code Mixed Dravidian LanguagesMudoor Devadas Anusha, Hosahalli Lakshmaiah Shashirekha. 996-1004 [doi]
• Sentiment Analysis Model For Code-Mixed Tamil LanguageN. Sripriya, S. Divya. 1005-1010 [doi]
• Sentiment Analysis of Dravidian-CodeMix LanguageAnkit Kumar Mishra, Sunil Saumya, Abhinav Kumar. 1011-1019 [doi]
• Multilingual Sentiment Analysis in Tamil Malayalam and Kannada code-mixed social media posts using MBERTAdaikkan Kalaivani, Durairaj Thenmozhi. 1020-1028 [doi]
• Deep Learning Based Sentiment analysis for MalayalamTamil and Kannada languagesPavan Kumar P. H. V, Premjith B, Sanjanasri J. P., Soman K. P. 1029-1037 [doi]
• Machine Learning Based Approach for Sentiment Analysis on Multilingual Code Mixing TextB. Bharathi, G. U. Samyuktha. 1038-1043 [doi]
• Sentiment Analysis on Tamil Code-Mixed Text using Bi-LSTMPradeep Kumar Roy, Abhinav Kumar. 1044-1050 [doi]
• Is Meta Embedding Better than Pre-Trained Word Embedding to Perform Sentiment Analysis for Dravidian Languages in Code-Mixed Text?Supriya Chanda, Rajat Pratap Singh, Sukomal Pal. 1051-1060 [doi]
• Voting Ensemble Model Based Malayalam-English Sentiment Analysis on Code-Mixed DataK. Nimmi, B. Janet. 1061-1068 [doi]
• Sentiment Analysis on Dravidian Code-Mixed YouTube Comments using Paraphrase XLM-RoBERTa ModelYandrapati Prakash Babu, Rajagopal Eswari. 1069-1076 [doi]
• Sentiment Analysis in Dravidian Code-Mixed YouTube Comments and PostsSanjeepan Sivapiran, Charangan Vasantharajan, Uthayasanker Thayasivam. 1077-1084 [doi]
• An Ensemble Model for Sentiment Classification on Code-Mixed Data in Dravidian LanguagesS. R. Mithun Kumar, Nihal Reddy, Aruna Malapati, Lov Kumar. 1085-1093 [doi]
• Sentiment Analysis using Cross Lingual Word Embedding ModelJerin Mahibha C, Kayalvizhi Sampath, Durairaj Thenmozhi. 1094-1100 [doi]
• Overview of the Shared Task on Fake News Detection in Urdu at FIRE 2021Maaz Amjad, Sabur Butt, Hamza Imam Amjad, Alisa Zhila, Grigori Sidorov, Alexander F. Gelbukh. 1101-1116 [doi]
• Ensembled Feature Selection for Urdu Fake News DetectionFazlourrahman Balouchzahi, Hosahalli Lakshmaiah Shashirekha, Grigori Sidorov. 1117-1126 [doi]
• Automatic Fake News Detection in Urdu Language using TransformersIqra Ameer, Claudia Porto Capetillo, Helena Gómez-Adorno, Grigori Sidorov. 1127-1134 [doi]
• Urdu Fake News Detection using TF-IDF Features and TextCNNMuhammad Abdullah Ilyas, Khurram Shahzad. 1135-1141 [doi]
• Ensembling Machine Learning Models for Urdu Fake News DetectionHammad Akram, Khurram Shahzad. 1142-1149 [doi]
• A Simple N-Gram Model for Urdu Fake News DetectionHamada Nayel, Ghada Amer. 1150-1155 [doi]
• The 2021 Urdu Fake News Detection Task using Supervised Machine Learning and Feature CombinationsMuhammad Humayoun. 1156-1161 [doi]
• Detecting Fake News in URDU using Classical Supervised Machine Learning Methods and Word/Char N-gramsYaakov HaCohen-Kerner, Natan Manor, Netanel Bashan, Elyasaf Dimant. 1162-1167 [doi]
• A Machine Learning Approach for Fake News Detection from Urdu Social Media PostsAbhinav Kumar, Jyoti Kumari. 1168-1174 [doi]
• Ensembling of Various Transformer Based Models for the Fake News Detection Task in the Urdu LanguageSakshi Kalraa, Preetika Vermaa, Yashvardhan Sharma, Gajendra Singh Chauhan. 1175-1181 [doi]
• Fake News Detection in Urdu Language using BERTSnehaan Bhawal, Pradeep Kumar Roy. 1182-1189 [doi]
• Urdu Fake News Detection Using Ensemble of Machine Learning ModelsAsha Hegde, Hosahalli Lakshmaiah Shashirekha. 1190-1198 [doi]
• VaccineBERT: BERT for COVID-19 Vaccine Tweet ClassificationShivangi Bithel, Samidha Verma. 1199-1203 [doi]
• Identifying Situational Information during Mass EmergencySumit Anand, Mehuly Chakraborthy, Diptaraj Sen. 1204-1209 [doi]
• COVID Vaccine Stance ClassificationMeghna Chakraborty, Sk. Aftab Aman. 1210-1215 [doi]
• Bidirectional Encoder Representations from Transformers for the COVID-19 vaccine stance classificationAbhinav Kumar, Pradeep Kumar Roy, Jyoti Prakash Singh. 1216-1220 [doi]
• Analyzing COVID-19 VaccinationPoulami Ghosh, Sayani Ghosh. 1221-1226 [doi]
• Retrieval of Actionable Information during Mass Emergency: A Classification ApproachAayush Chowdhury, Aman Choudhary, Aishwarya Roy. 1227-1232 [doi]
• Overview of the Causality-driven Adhoc Information Retrieval (CAIR) task at FIRE-2021Suchana Datta, Debasis Ganguly, Dwaipayan Roy, Derek Greene. 1233-1237 [doi]
• Causal Document Retrieval: An Analytical InvestigationPankaj Dadure, Partha Pakray, Sivaji Bandyopadhyay. 1238-1245 [doi]
• A Semantic Search Pipeline for Causality-driven Adhoc Information RetrievalDhairya Dalal, Sharmi Dev Gupta, Bentolhoda Binaei. 1246-1254 [doi]