We are interested in studying method to determine the attitude expressed in a text towards a topic (stance detection), such as determining if a tweet expresses a positive, negative or neutral stance towards a political entity. One additional challenge we are exploring is stance detection in a conversational context, where the stance depends on the context of the conversation. Fact checking using textual data can be framed very similarly, namely as if an evidence document agrees with, disagrees with or is topically unrelated to a headline or claim.
We are also researching the relationship between attitudes towards entities on social media and gender bias as part of a project funded by DFF.
