Dimitar Alexandrov

Ramy Baly, Georgi Karadzhov, Dimitar Alexandrov et al.

We present a study on predicting the factuality of reporting and bias of news media. While previous work has focused on studying the veracity of claims or documents, here we are interested in characterizing entire news media. These are under-studied but arguably important research problems, both in their own right and as a prior for fact-checking systems. We experiment with a large list of news websites and with a rich set of features derived from (i) a sample of articles from the target news medium, (ii) its Wikipedia page, (iii) its Twitter account, (iv) the structure of its URL, and (v) information about the Web traffic it attracts. The experimental results show sizable performance gains over the baselines, and confirm the importance of each feature type.

Valentin Zmiycharov, Dimitar Alexandrov, Preslav Nakov et al.

We describe the system that our FMI@SU student's team built for participating in the Triple Scoring task at the WSDM Cup 2017. Given a triple from a "type-like" relation, profession or nationality, the goal is to produce a score, on a scale from 0 to 7, that measures the relevance of the statement expressed by the triple: e.g., how well does the profession of an Actor fit for Quentin Tarantino? We propose a distant supervision approach using information crawled from Wikipedia, DeletionPedia, and DBpedia, together with task-specific word embeddings, TF-IDF weights, and role occurrence order, which we combine in a linear regression model. The official evaluation ranked our submission 1st on Kendall's Tau, 7th on Average score difference, and 9th on Accuracy, out of 21 participating teams.