Please note: This master’s thesis presentation will be given online.
Saira
Rizvi,
Master’s
candidate
David
R.
Cheriton
School
of
Computer
Science
|
Department
of
Statistics
and
Actuarial
Science
Supervisor: Professor Charles Clarke
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.
To join this master’s thesis presentation on Zoom, please go to https://us06web.zoom.us/j/87871625995?pwd=MXFTd2tJdmZKVlRRamw3WmJXRVBrZz09.