A5061017602- Misinformation and Its Impacts
- Hate Speech and Cyberbullying Detection
- Spam and Phishing Detection
- Topic Modeling
- Sentiment Analysis and Opinion Mining
- Social Media and Politics
- Semantic Web and Ontologies
- Neural Networks and Applications
- Internet Traffic Analysis and Secure E-voting
- Advanced Database Systems and Queries
- Media, Religion, Digital Communication
- Data-Driven Disease Surveillance
- Complex Network Analysis Techniques
- Advanced Malware Detection Techniques
- Digital Marketing and Social Media
- Evolutionary Algorithms and Applications
- Service-Oriented Architecture and Web Services
- Public Relations and Crisis Communication
- Neural dynamics and brain function
- Influenza Virus Research Studies
- Freedom of Expression and Defamation
- Media Influence and Politics
- Digital Mental Health Interventions
- Ethics and Social Impacts of AI
- Data Management and Algorithms
Mannheim University of Applied Sciences
2025
University of Duisburg-Essen
2020-2024
University of Trento
2018-2019
During the COVID-19 pandemic, social media has become a home ground for misinformation. To tackle this infodemic, scientific oversight, as well better understanding by practitioners in crisis management, is needed. We have conducted an exploratory study into propagation, authors and content of misinformation on Twitter around topic order to gain early insights. collected all tweets mentioned verdicts fact-checked claims related over 92 professional fact-checking organisations between January...
The HASOC track is dedicated to the evaluation of technology for finding Offensive Language and Hate Speech. creating a multilingual data corpus mainly English under-resourced languages(Hindi Marathi). This paper presents one subtrack with two tasks. In 2021, we organized classification task English, Hindi, Marathi. first consists tasks; Subtask 1A binary fine-grained into offensive non-offensive tweets. 1B asks classify tweets Hate, Profane offensive. Task 2 identifying given additional...
In this paper, we present a first multilingual cross-domain dataset of 5182 fact-checked news articles for COVID-19, collected from 04/01/2020 to 15/05/2020. We have the 92 different fact-checking websites after obtaining references Poynter and Snopes. manually annotated into 11 categories according their content. The is in 40 languages 105 countries. built classifier detect fake results automatic detection its class. Our model achieves an F1 score 0.76 false class other fact check articles....
There is currently no easy way to discover potentially problematic content on WhatsApp and other end-to-end encrypted platforms at scale. In this paper, we analyze the usefulness of a crowd-sourced tipline through which users can submit (“tips”) that they want fact-checked. We compared tips sent run during 2019 Indian general election with messages circulating in large, public groups social media same period. found tiplines are very useful lens into conversations: significant fraction images...
With the growth of social media, spread hate speech is also increasing rapidly. Social media are widely used in many countries. Also Hate Speech spreading these This brings a need for multilingual detection algorithms. Much research this area dedicated to English at moment. The HASOC track intends provide platform develop and optimize algorithms Hindi, German English. dataset collected from Twitter archive pre-classified by machine learning system. has two sub-task all three languages: task...
The 2023 Israel-Hamas conflict began in early October 2023, and as the progressed, various instances of fake news spreading worldwide. Fake appeared on different platforms was multiple languages. For scientific study News, claims, propagation, we contribute first publicly available dataset social media posts, fact-checked articles war. WarClaim data is collected from 131 fact-checking organisations 40 We explain collection approach source dataset. enriched by providing information used...
Twitter is becoming an increasingly important platform for disseminating information during crisis situations, such as the COVID-19 pandemic.Effective communication on can shape public perception of crisis, influence adherence to preventative measures, and thus affect health.Influential accounts are particularly they reach large audiences quickly.This study identifies influential German-language from almost 3 million German tweets collected between January May 2020 by constructing a retweet...
During the outbreak of COVID-19 pandemic, many people shared their symptoms across Online Social Networks (OSNs) like Twitter, hoping for others' advice or moral support. Prior studies have shown that those who disclose health-related information OSNs often tend to regret it and delete publications afterwards. Hence, deleted posts containing sensitive data can be seen as manifestations online regrets. In this work, we present an analysis content on Twitter during pandemic. For this,...
There is currently no easy way to fact-check content on WhatsApp and other end-to-end encrypted platforms at scale. In this paper, we analyze the usefulness of a crowd-sourced "tipline" through which users can submit ("tips") that they want fact-checked. We compare tips sent tipline run during 2019 Indian national elections with messages circulating in large, public groups social media same period. find tiplines are very useful lens into conversations: significant fraction images match being...