CONSTRAINT 2021

First Workshop on ​Combating On​line Ho​st​ile Posts in ​Regional L​anguages dur​ing Emerge​ncy Si​tuation

Collocated with AAAI 2021

8th February, 2021


NEWS

ABOUT THE WORKSHOP

The increasing accessibility of the Internet has dramatically changed the way we consume information. The ease of social media usage not only encourages individuals to freely express their opinion (freedom of speech) but also provides content polluters with ecosystems to spread hostile posts (hate speech, fake news, cyberbullying, etc.). Such hostile activities are expected to increase manifold during emergencies such as the 2021 US presidential election, COVID-19 pandemic spreading. Most of such hostile posts are written in regional languages, and therefore can easily evade online surveillance engines, the majority of which are trained on the posts written in resource-rich languages such as English and Chinese. Therefore, regions such as Asia, Africa, South America, where low-resource ​regional languages are used for day-to-day communication, suffer due to the lack of tools, benchmark datasets and learning techniques. Other developing countries such as Italy, Spain, where the used languages (pseudo-low-resource) are not as equipped with sophisticated computational resources as English, might also be facing the same issues.
CONSTRAINT’21 will encourage researchers from interdisciplinary domains working on multilingual social media analytics to think beyond the conventional way of combating online hostile posts. The workshop will emphasise on three major points:

  1. Regional language: The offensive posts under inspection may be written in low-resource regional languages (e.g., Tamil, Urdu, Bangali, Polish, Czech, Lithuanian, etc.).
  2. Emergency situation: ​The proposed solutions should be able to tackle misinformation during emergency situations where due to the lack of enough historical data, learning models need to adopt additional intelligence to handle emerging and novel posts.
  3. Early detection: ​Since the effect of misinformation during emergency situations is highly detrimental for society (e.g., health-related misadvice during a pandemic may take human’s life), we encourage the solutions to be able to detect such hostile posts as early as possible after their appearance on social media.

CALL FOR SUBMISSIONS


REGULAR PAPER SUBMISSION

  • Topics of Interests: We invite the submission of high-quality manuscripts reporting relevant research in the area of collecting, managing, mining, and understanding hostile data from social media platforms. Topics of interest include, but are not limited to:
    • Fake news detection in regional languages
    • Hate speech detection in regional languages
    • Evolution of fake news and hate speech
    • Analyzing user behavior for hostile post propagation
    • Real-world tool development for combating hostile posts
    • Psychological study of the spreaders of hostile posts
    • Hate speech normalization
    • Information extraction, ontology design and knowledge graph for combating hostile posts
    • Early detection for hostile posts
    • Design lightweight tools with less data for hostile post detection
    • Code-mixed and code-switched hostile post analysis
    • Open benchmark and dashboard related to regional hostile posts
    • Specific case studies and surveys related to hostile posts
    • Claim detection and verification related to misinformation
    • Fact-check worthiness of misinformation
    • Cross-region language analysis for hostile posts
    • Computational social science analysis for hostile posts
    • Network analysis for fake news spreading and evolution

  • Submission Instructions:
    • Regular papers: Regular papers (maximum 12 pages including references) should be prepared in English and follow the Springer CCIS template, downloadable from here. Papers will be published in Springer CCIS.
    • Previously rejected papers: If your paper has been rejected from EMNLP/NeurIPS or AAAI 2021 regular track, you can also submit the peer reviews, ratings and 1 page cover letter of the changes made to address those comments. Note that these things should be in a separate file and NOT appended to the submitted manuscript. These papers will be reviewed separately.
    • Extended abstracts: We also invite extended abstract submission. The length of extended abstracts is 2 pages, including figures and tables (references not included). Note that there will be no published proceedings for extended abstracts. Work in progress or accepted papers are encouraged.
    • All papers must be submitted via our EasyChair submission page. Regular papers will go through a double-blind peer-review process. Extended abstracts may be either single blind (i.e., reviewers are blind, authors have names on submission) or double blind (i.e., authors and reviewers are blind). Only manuscripts in PDF or Microsoft Word format will be accepted.

  • Important Dates:
    • Dec 5, 2020: Research papers and extended abstracts due at 11:59 PM UTC-12
    • Dec 20, 2020: Notification of papers due at 11:59 PM UTC-12
    • Dec 28, 2020: Camera ready submission due of accepted papers at 11:59 PM UTC-12
    • Feb 8, 2021: CONSTRAINT workshop

SHARED TASK

  • Task: We are planning to release two types of datasets:
    • COVID19 Fake news detection (English)
    • Hostility detection (Hindi)
    For more details and updates please visit https://constraint-shared-task-2021.github.io/

  • Important Dates:
    • October 1, 2020: Release of the training set
    • December 1, 2020: Release of the test set
    • December 10, 2020: Deadline for submitting the final results
    • December 12, 2020: Announcement of the results
    • December 25, 2020: System paper submission deadline (All teams are invited to submit a paper)

JOURNAL SPECIAL ISSUE

Selected workshop papers will be invited for an extension to be considered in a special issue of Neurocomputing journal (Impact factor: 4.438)

AWARDS

    Best main track paper
  • Omar Sharif and Mohammed Moshiul Hoque. Identification and Classification of Textual Aggression in Social Media: Resource Creation and Evaluation
    Shared task best paper
  • Ben Chen, Bin Chen, Dehong Gao, Qijin Chen, Xiaonan Meng, Chengfu Huo, Yang Zhou and Weijun Ren. Transformer-based Language Model Fine-tuning Methods for COVID-19 Fake News Detection
    English shared task winner
  • Anna Glazkova, Maksim Glazkov and Timofey Trifonov. g2tmn at Constraint@AAAI2021: Exploiting CT-BERT and Ensembling Learning for COVID-19 Fake News Detection
    Hindi coarse grained sub task winner
  • Tathagata Raha, Sayar Ghosh Roy, Ujwal Narayan, Zubair Abid and Vasudeva Varma. Task Adaptive Pretraining of Transformers for Hostility Detection
    Hindi Fine grained winner
  • Siyao Zhou, Rui Fu and Jie Li. Fake news and hostile post detection using an ensemble learning mode.
    Best reviewer
  • Sarah Masud
    Shared task best paper honorable mention
  • Arkadipta De, Venkatesh E, Kaushal Kumar Maurya and Maunendra Sankar Desarkar. Coarse and Fine-Grained Hostility Detection in Hindi Posts using Fine Tuned Multilingual Embeddings

Accepted Papers

Title Authors
Identifying Offensive Content in Social Media Posts Ashwin Singh and Rudraroop Ray
Identification and Classification of Textual Aggression in Social Media: Resource Creation and Evaluation Omar Sharif and Mohammed Moshiul Hoque
Fighting an Infodemic: COVID-19 Fake News Dataset Parth Patwa, Shivam Sharma, Srinivas Pykl, Vineeth Guptha, Gitanjali Kumari, Md Shad Akhtar, Asif Ekbal, Amitava Das and Tanmoy Chakraborty
Revealing the Blackmarket Retweet Game: A Hybrid Approach Shreyash Arya and Hridoy Sankar Dutta
Overview of CONSTRAINT 2021 Shared Tasks: Detecting English COVID-19 Fake News and Hindi Hostile Posts Parth Patwa, Mohit Bhardwaj, Vineeth Guptha, Gitanjali Kumari, Shivam Sharma, Srinivas P Y K L, Amitava Das, Asif Ekbal, Md. Shad Akhtar and Tanmoy Chakraborty
LaDiff ULMFiT: A Layer Differentiated training approach for ULMFiT Mohammed Azhan and Mohammad Ahmad
Extracting latent information from datasets in The CONSTRAINT-2020 shared task on the hostile post detection Renyuan Liu and Xiaobing Zhou
Fake news and hostile posts detection using an ensemble learning model Siyao Zhou, Rui Fu and Jie Li
Transformer-based Language Model Fine-tuning Methods for COVID-19 Fake News Detection Ben Chen, Bin Chen, Dehong Gao, Qijin Chen, Xiaonan Meng, Chengfu Huo, Yang Zhou and Weijun Ren
Tackling the infodemic : Analysis using Transformer based models Anand Zutshi and Aman Raj
Exploring Text-transformers in AAAI 2021 Shared Task: COVID-19 Fake News Detection in English Xiangyang Li, Yu Xia, Long Xiang, Zheng Li and Sujian Li
g2tmn at Constraint@AAAI2021: Exploiting CT-BERT and Ensembling Learning for COVID-19 Fake News Detection Anna Glazkova, Maksim Glazkov and Timofey Trifonov
Model Generalization on COVID-19 Fake News Detection Yejin Bang, Etsuko Ishii, Samuel Cahyawijaya, Ziwei Ji and Pascale Fung
ECOL: Early Detection of COVID Lies Using Content, Prior Knowledge and Source Information Ipek Baris and Zeyd Boukhers
Evaluating Deep Learning Approaches for Covid19 Fake News Detection Apurva Wani, Isha Joshi, Snehal Khandve, Vedangi Wagh and Raviraj Joshi
A Heuristic-driven Ensemble Framework for COVID-19 Fake News Detection Sourya Dipta Das, Ayan Basak and Saikat Dutta
Identification of COVID-19 related Fake News via Neural Stacking Boshko Koloski, Timen Stepišnik-Perdih and Blaž Škrlj
Fake News Detection System using XLNet model with Topic Distributions: CONSTRAINT@AAAI2021 Shared Task Venktesh V, Akansha Gautam and Sarah Masud
Coarse and Fine-Grained Hostility Detection in Hindi Posts using Fine Tuned Multilingual Embeddings Arkadipta De, Venkatesh E, Kaushal Kumar Maurya, and Maunendra Sankar Desarkar
Hostility Detection in Hindi leveraging Pre-Trained Language Models Ojasv Kamal, Adarsh Kumar, and Tejas Vaidhya
Stacked embeddings and multiple fine-tuned XLM-RoBERTa models for Enhanced hostility identification Siva Sai, Alfred W. Jacob, Sakshi Kalra, and Yashvardhan Sharma
Task Adaptive Pretraining of Transformers for Hostility Detection Tathagata Raha, Sayar Ghosh Roy, Ujwal Narayan, Zubair Abid, and Vasudeva Varma
Divide and Conquer: An Ensemble Approach for Hostile Post Detection in Hindi Varad Bhatnagar, Prince Kumar, Sairam Moghili and Pushpak Bhattacharyya

Non-Archival Papers

Title Authors Link
Combating Hostility: Covid-19 Fake News and Hostile Post Detection in Social Media Omar Sharif, Eftekhar Hossain, Mohammed Moshiul Hoque https://arxiv.org/abs/2101.03291
Transformer based Automatic COVID-19 Fake News Detection System Sunil Gundapu, Radhika Mamidi https://arxiv.org/abs/2101.00180
Evaluation of Deep Learning Models for Hostility Detection in Hindi Text Ramchandra Joshi, Rushabh Karnavat, Kaustubh Jirapure, Raviraj Joshi https://arxiv.org/abs/2101.04144
Constraint 2021: Machine Learning Models for COVID-19 Fake News Detection Shared Task Thomas Felber https://arxiv.org/abs/2101.03717
Walk in Wild: An Ensemble Approach for Hostility Detection in Hindi Posts Chander Shekhar, Bhavya Bagla, Kaushal Kumar Maurya, Maunendra Sankar Desarkar https://arxiv.org/abs/2101.06004
A transformer based approach for fighting COVID-19 fake news S.M. Sadiq-Ur-Rahman Shifath, Mohammad Faiyaz Khan, Md. Saiful Islam https://arxiv.org/abs/2101.12027
Identifying COVID-19 Fake News in Social Media Tathagata Raha, Vijayasaradhi Indurthi, Aayush Upadhyaya, Jeevesh Kataria, Pramud Bommakanti, Vikram Keswani, Vasudeva Varma https://arxiv.org/abs/2101.11954
TUDublin team at Constraint@AAAI2021 - COVID19 Fake News Detection Elena Shushkevich, John Cardiff https://arxiv.org/abs/2101.05701
Detecting Hostile Posts using Relational Graph Convolutional Network Sarthak, Shikhar Shukla, Govind Mittal, Karm Veer Arya https://arxiv.org/abs/2101.03485
Hostility Detection and Covid-19 Fake News Detection in Social Media Ayush Gupta, Rohan Sukumaran, Kevin John, Sundeep Teki https://arxiv.org/abs/2101.05953

AGENDA

Keynote

Workshop paper presentation (2)

Panel Discussion

Break

Shared task overview

Workshop paper presentation (2)

Shared task papers (9)

Break

Shared task papers (9)

Best paper announcement and vote of thanks

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