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Improving Health Mentioning Classification of Tweets using Contrastive Adversarial Training [article]

Pervaiz Iqbal Khan, Shoaib Ahmed Siddiqui, Imran Razzak, Andreas Dengel, Sheraz Ahmed
2022 arXiv   pre-print
Health mentioning classification (HMC) classifies an input text as health mention or not. Figurative and non-health mention of disease words makes the classification task challenging.  ...  In this paper, we improve the word representation of the input text using adversarial training that acts as a regularizer during fine-tuning of the model.  ...  Experiments results show that adversarial and contrastive training significantly improves the tweet health mentioning classification performance over the baseline methods.  ... 
arXiv:2203.01895v1 fatcat:s2mrlsvjx5csxoxmvw4o6snn4u

A Novel Approach to Train Diverse Types of Language Models for Health Mention Classification of Tweets [article]

Pervaiz Iqbal Khan, Imran Razzak, Andreas Dengel, Sheraz Ahmed
2022 arXiv   pre-print
In this paper, we propose a novel approach to train language models for health mention classification of tweets that involves adversarial training.  ...  Health mention classification deals with the disease detection in a given text containing disease words. However, non-health and figurative use of disease words adds challenges to the task.  ...  Finally, we present how we combine adversarial training with the Barlow Twins loss for the health mention classification of tweets.  ... 
arXiv:2204.06337v1 fatcat:3m7hy5y33nf25ed2xgiezzt5a4

Cross-SEAN: A Cross-Stitch Semi-Supervised Neural Attention Model for COVID-19 Fake News Detection [article]

William Scott Paka, Rachit Bansal, Abhay Kaushik, Shubhashis Sengupta, Tanmoy Chakraborty
2021 arXiv   pre-print
We also develop Chrome-SEAN, a Cross-SEAN based chrome extension for real-time detection of fake tweets.  ...  This calls for an immediate need to contain the spread of such misinformation on social media. We introduce CTF, the first COVID-19 Twitter fake news dataset with labeled genuine and fake tweets.  ...  Chakraborty would like to thank the generous support of the Ramanujan Fellowship (SERB) and Infosys Centre for AI, IIIT Delhi.  ... 
arXiv:2102.08924v3 fatcat:kywf6pc24zgwxosnz2r56cabz4

"Is depression related to cannabis?": A knowledge-infused model for Entity and Relation Extraction with Limited Supervision [article]

Kaushik Roy, Usha Lokala, Vedant Khandelwal, Amit Sheth
2021 arXiv   pre-print
Because of the lack of annotations due to the limited availability of the domain experts' time, we use supervised contrastive learning in conjunction with GPT-3 trained on a vast corpus to achieve improved  ...  With strong marketing advocacy of the benefits of cannabis use for improved mental health, cannabis legalization is a priority among legislators.  ...  From the above comparison using contrastive learning with knowledge, infused learning can perform better in relation classification.  ... 
arXiv:2102.01222v1 fatcat:ej5mcjrduvbmlnoxxv3gaimkju

Towards Explainable Fact Checking [article]

Isabelle Augenstein
2021 arXiv   pre-print
about public health.  ...  This development has spurred research in the area of automatic fact checking, from approaches to detect check-worthy claims and determining the stance of tweets towards claims, to methods to determine  ...  mentioned in the tweets.  ... 
arXiv:2108.10274v2 fatcat:5s4an6irezcjfmvvhmiaeqarh4

Deceptive Deletions for Protecting Withdrawn Posts on Social Platforms [article]

Mohsen Minaei, S Chandra Mouli, Mainack Mondal, Bruno Ribeiro, Aniket Kate
2020 arXiv   pre-print
In many of these cases, users regret posting such content.  ...  We show that a powerful global adversary can be beaten by a powerful challenger, raising the bar significantly and giving a glimmer of hope in the ability to be really forgotten on social platforms.  ...  The challenger, in direct contrast to the adversary, wishes to decrease adversary's precision and recall for the classification of deleted posts.  ... 
arXiv:2005.14113v1 fatcat:x4c5qtregrd4tkhqib5e7ckb2e

Empathy and Hope: Resource Transfer to Model Inter-country Social Media Dynamics [article]

Clay H. Yoo, Shriphani Palakodety, Rupak Sarkar, Ashiqur R. KhudaBukhsh
2021 arXiv   pre-print
Against the backdrop of a contentious history including four wars, divisive content of this nature, especially when a country is facing an unprecedented healthcare crisis, fuels further deterioration of  ...  We also release the first publicly available data set at the intersection of geopolitical relations and a raging pandemic in the context of India and Pakistan.  ...  Resource: We present a data set of tweets exploring geopolitical relations between historic adversaries amidst a health crisis.  ... 
arXiv:2106.12044v1 fatcat:tcavskmnlreeddpbayjg6i6upu

Arabic Fake News Detection: Comparative Study of Neural Networks and Transformer-Based Approaches

Maha Al-Yahya, Hend Al-Khalifa, Heyam Al-Baity, Duaa AlSaeed, Amr Essam, M. Irfan Uddin
2021 Complexity  
., the use of neural networks and transformers. This paper presents a comprehensive comparative study of neural network and transformer-based language models used for Arabic FND.  ...  We examine the use of neural networks and transformer-based language models for Arabic FND and show their performance compared to each other.  ...  Acknowledgments is research project was supported by a grant from the "Research Center of the Female Scientific and Medical Colleges," Deanship of Scientific Research, King Saud University, Riyadh, Saudi  ... 
doi:10.1155/2021/5516945 fatcat:4of6srfkkbfsxazmpv5pojmnw4

Training a text classifier with a single word using Twitter Lists and domain adaptation

Aron Culotta
2016 Social Network Analysis and Mining  
A classifier is then fit to the exemplar accounts and used to predict labels of new tweets and users.  ...  on four of six datasets despite using no manually labeled data.  ...  Any opinions, findings and conclusions or recommendations expressed in this material are the authors' and do not necessarily reflect those of the sponsor.  ... 
doi:10.1007/s13278-016-0317-1 fatcat:4ur3yqhwava67p4qtlnncj2ttm

A comparative study of Bot Detection techniques methods with an application related to Covid-19 discourse on Twitter [article]

Marzia Antenore, Jose M. Camacho-Rodriguez, Emanuele Panizzi
2021 arXiv   pre-print
In addition, it was analyzed the presence of bots in tweets from different periods of the first months of the Covid-19 pandemic, using the bot detection technique which best fits the scope of the task.  ...  The techniques utilized to elaborate the bot detection models include the utilization of features as the tweets metadata or the Digital Fingerprint of the Twitter accounts.  ...  It is worth to mention that the datasets used for training are the same that in [75] , whilst for testing, the datasets stock and kaiser are added to the datasets already used in [75] .  ... 
arXiv:2102.01148v1 fatcat:n6ur2qtdfjek3gdplyiwjhqb3m

Rumor Detection on Social Media with Graph Adversarial Contrastive Learning

Tiening Sun, Zhong Qian, Sujun Dong, Peifeng Li, Qiaoming Zhu
2022 Proceedings of the ACM Web Conference 2022  
These adversarial samples are also used as hard negative samples in contrastive learning to make the model more robust and effective.  ...  In this paper, we propose a novel Graph Adversarial Contrastive Learning (GACL) method to fight these complex cases, where the contrastive learning is introduced as part of the loss function for explicitly  ...  This research was supported by the National Natural Science Foundation of China (Nos. 61836007 and 62006167), and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD  ... 
doi:10.1145/3485447.3511999 fatcat:xmag5jlpznejnjoumtiblgo2t4

COVID-19 Public Sentiment Insights and Machine Learning for Tweets Classification [article]

Jim Samuel, G. G. Md. Nawaz Ali, Md. Mokhlesur Rahman, Ek Esawi, Yana Samuel
2020 arXiv   pre-print
We observe a strong classification accuracy of 91% for short Tweets, with the Naive Bayes method.  ...  Tweets of varying lengths.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
arXiv:2005.10898v1 fatcat:zsqftqos65az3olcgy6gepxbs4

COVID-19 Public Sentiment Insights and Machine Learning for Tweets Classification

Jim Samuel, G. G. Md. Nawaz Ali, Md. Mokhlesur Rahman, Ek Esawi, Yana Samuel
2020 Information  
We observe a strong classification accuracy of 91% for short Tweets, with the Naïve Bayes method.  ...  Tweets of varying lengths.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/info11060314 fatcat:4raybo6qbvdxde5opxbdu6ab3q

Detection of spam-posting accounts on Twitter

Isa Inuwa-Dutse, Mark Liptrott, Ioannis Korkontzelos
2018 Neurocomputing  
In contrast to prior research findings, we observe that an average automated spam account posted at least 12 tweets per day at well defined periods.  ...  The rapid growth in the volume of global spam is expected to compromise research works that use social media data, thereby questioning data credibility.  ...  Francesco Rizzuto for the fruitful discussions and exchange of ideas about a multitude of aspects related to social media, spam content and the motives of spammers.  ... 
doi:10.1016/j.neucom.2018.07.044 fatcat:feui2u55ujhanaam22hfzeuyvi

"When they say weed causes depression, but it's your fav antidepressant": Knowledge-aware attention framework for relationship extraction

Shweta Yadav, Usha Lokala, Raminta Daniulaityte, Krishnaprasad Thirunarayan, Francois Lamy, Amit Sheth, Robert Hoehndorf
2021 PLoS ONE  
Our model is further tailored to provide more focus to the entities mention in the sentence through entity-position aware attention layer, where ontology is used to locate the target entities position.  ...  With the increasing legalization of medical and recreational use of cannabis, more research is needed to understand the association between depression and consumer behavior related to cannabis consumption  ...  The DAO was expanded using DSM-5 categories covering the most common mental health disorders by utilizing the study of [25] for improving data collection about mental health and cannabis use on Twitter  ... 
doi:10.1371/journal.pone.0248299 pmid:33764983 fatcat:awylmujzffc5tjxvhy75bnfbje
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