Development of Clickbait Classifier based on Detecting the Stance of Headlines to Articles

Student: Kim Darya

Faculty: HSE Tikhonov Moscow Institute of Electronics and Mathematics (MIEM HSE)

Educational Programme: Applied Mathematics (Bachelor)

Year of Graduation: 2019

Due to the popularity of social networks and personalized search algorithms, it becomes more difficult for users to evaluate media quality and resist disinformation. In order to facilitate this task, new methods for automated fact checking are required. The first step to solving the problem of automatic news verification is automated clickbait filtering. This paper presents a dataset of 11 thousand news for clickbait classification and provides machine learning classification baseline. In addition, it was proved that the use of comparative characteristics of headings to texts, or stance, for training helps to improve models' performances.

Student Theses at HSE must be completed in accordance with the University Rules and regulations specified by each educational programme.

Summaries of all theses must be published and made freely available on the HSE website.

The full text of a thesis can be published in open access on the HSE website only if the authoring student (copyright holder) agrees, or, if the thesis was written by a team of students, if all the co-authors (copyright holders) agree. After a thesis is published on the HSE website, it obtains the status of an online publication.

Student theses are objects of copyright and their use is subject to limitations in accordance with the Russian Federation’s law on intellectual property.

In the event that a thesis is quoted or otherwise used, reference to the author’s name and the source of quotation is required.

Search all student theses