Misinformation Retrieval

This work introduces the task of misinformation retrieval, identifying all documents containing misinformation for a given topic, and proposes a pipeline for misinformation retrieval on tweets. As part of the work, I curated 50 COVID-19 misinformation topics used in the TREC 2020 Health Misinformation track. In addition, I annotated a test set of tweets using the TREC COVID-19 misinformation on social media. Misinformation on social media has proven highly detrimental to communities by encouraging harmful and often life-threatening behavior. The chaos caused by COVID-19 misinformation has created an urgent need for misinformation detection methods to moderate social media platforms. Drawing upon previous work in misinformation detection and the TREC 2020 Health Misinformation Track, I focused on the task of misinformation retrieval on social media. I extended the COVID-Lies data set created to detect COVID-19 misinformation in tweets by rephrasing the misconceptions accompanying each tweet. I also created 50 COVID-19 related topics for the TREC 2020 Health Misinformation track used for evaluation purposes. I propose a natural language inference (NLI) based approach using CT-BERT to identify tweets that contradict a given fact, used to score documents utilizing the model’s classification probability. The model was trained using a combination of NLI data sets to find the best approach. Tweets were labeled for the TREC 2020 Health Misinformation Track topics to create a test set on which the best model achieves an AUC of 0.81. I conducted several experiments which show that domain adaptation significantly improved the ability to detect misinformation. A combination of a large NLI corpus, such as SNLI, and an in-domain, such as the COVID-Lies, data set achieves the best performance on our test set. The pipelines retrieved and ranked tweets based on misinformation for 7 TREC topics from the COVID-19 Twitter stream. The top 20 unique tweets were analyzed using Precision@20 to evaluate the pipeline

Geographic information extraction from texts

A large volume of unstructured texts, containing valuable geographic information, is available online. This information – provided implicitly or explicitly – is useful not only for scientific studies (e.g., spatial humanities) but also for many practical applications (e.g., geographic information retrieval). Although large progress has been achieved in geographic information extraction from texts, there are still unsolved challenges and issues, ranging from methods, systems, and data, to applications and privacy. Therefore, this workshop will provide a timely opportunity to discuss the recent advances, new ideas, and concepts but also identify research gaps in geographic information extraction