Critical Digital Literacy

@article{gurgun2024we,

title = {Why do we not stand up to misinformation? Factors influencing the likelihood of challenging misinformation on social media and the role of demographics},

author = {Selin Gurgun and Deniz Cemiloglu and Emily Arden Close and Keith Phalp and Preslav Nakov and Raian Ali},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {Technology in Society},

volume = {76},

pages = {102444},

publisher = {Elsevier},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{hasanain2024can,

title = {Can GPT-4 Identify Propaganda? Annotation and Detection of Propaganda Spans in News Articles},

author = {Maram Hasanain and Fatema Ahmed and Firoj Alam},

url = {https://arxiv.org/pdf/2402.17478.pdf},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {arXiv preprint arXiv:2402.17478},

abstract = {The use of propaganda has spiked on mainstream and social media, aiming to manipulate or mislead users. While efforts to automatically detect propaganda techniques in textual, visual, or multimodal content have increased, most of them primarily focus on English content. The majority of the recent initiatives targeting medium to low-resource languages produced relatively small annotated datasets, with a skewed distribution, posing challenges for the development of sophisticated propaganda detection models. To address this challenge, we carefully develop the largest propaganda dataset to date, ArPro, comprised of 8K paragraphs from newspaper articles, labeled at the text span level following a taxonomy of 23 propagandistic techniques. Furthermore, our work offers the first attempt to understand the performance of large language models (LLMs), using GPT-4, for fine-grained propaganda detection from text. Results showed that GPT-4's performance degrades as the task moves from simply classifying a paragraph as propagandistic or not, to the fine-grained task of detecting propaganda techniques and their manifestation in text. Compared to models fine-tuned on the dataset for propaganda detection at different classification granularities, GPT-4 is still far behind. Finally, we evaluate GPT-4 on a dataset consisting of six other languages for span detection, and results suggest that the model struggles with the task across languages. Our dataset and resources will be released to the community.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{noman2023challenging,

title = {Challenging others when posting misinformation: a UK vs. Arab cross-cultural comparison on the perception of negative consequences and injunctive norms},

author = {Muaadh Noman and Selin Gurgun and Keith Phalp and Preslav Nakov and Raian Ali},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

journal = {Behaviour & Information Technology},

pages = {1–21},

publisher = {Taylor & Francis},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{hasanain2023large,

title = {Large Language Models for Propaganda Span Annotation},

author = {Maram Hasanain and Fatema Ahmed and Firoj Alam},

url = {https://arxiv.org/abs/2311.09812},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

journal = {arXiv preprint arXiv:2311.09812},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{abdelali2023benchmarking,

title = {Benchmarking arabic ai with large language models},

author = {Ahmed Abdelali and Hamdy Mubarak and Shammur Absar Chowdhury and Maram Hasanain and Basel Mousi and Sabri Boughorbel and Yassine El Kheir and Daniel Izham and Fahim Dalvi and Majd Hawasly and others},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

journal = {arXiv preprint arXiv:2305.14982},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{dalvi2023llmebench,

title = {LLMeBench: A Flexible Framework for Accelerating LLMs Benchmarking},

author = {Fahim Dalvi and Maram Hasanain and Sabri Boughorbel and Basel Mousi and Samir Abdaljalil and Nizi Nazar and Ahmed Abdelali and Shammur Absar Chowdhury and Hamdy Mubarak and Ahmed Ali and others},

url = {https://arxiv.org/pdf/2308.04945},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

journal = {arXiv preprint arXiv:2308.04945},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{noman2023challengingb,

title = {Challenging others when posting misinformation: a UK vs. Arab cross-cultural comparison on the perception of negative consequences and injunctive norms},

author = {Muaadh Noman and Selin Gurgun and Keith Phalp and Preslav Nakov and Raian Ali},

url = {https://www.tandfonline.com/doi/epdf/10.1080/0144929X.2023.2298306?needAccess=true},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

journal = {Behaviour & Information Technology},

pages = {1–21},

publisher = {Taylor & Francis},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{gurgun4600006challenging,

title = {Challenging Misinformation on Social Media: Users' Perceptions and Misperceptions and Their Impact on the Likelihood to Challenge},

author = {Selin Gurgun and Deniz Cemiloglu and Emily Arden-Close and Keith Phalp and Raian Ali and Preslav Nakov},

url = {https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4600006},

journal = {Available at SSRN 4600006},

keywords = {},

pubstate = {published},

tppubtype = {article}

}