Abstract:
At present, Social media networks are widely used for
information sharing among billions of people around the globe.
However, the credibility of information shared through social
media is questionable because the sharing mechanisms used are
endless and the initiator of a news item is often unknown. This
results in sharing of inaccurate information since the users of
social media networks share news posts without varying the
authenticity and the accuracy. In order to address this issue, a
novel approach to calculate the accuracy level of news posts is
proposed in the research paper. The aim of this research project
is to provide an accuracy level for a social media news post that
is posted as a status update by a user. The proposed system
extracts the content of the news item, searches the Internet to find
similar articles in reliable online news sources, matches the
extracted content with the content of the news sites and generates
an accuracy level. In developing the system, Natural Language
Processing techniques such as web scraping techniques, web
crawling techniques, URL ranking methodologies, automatic text
summarization techniques and semantic analysis techniques such
as Word2vec and cosine similarity are used. After implementing
our system, we have gained a 70% of accuracy of relevancy of
news posts on social media with compared to the reliable online
news sources.
Citation:
R. Chandrathlake, L. Ranathunga, S. Wijethunge, P. Wijerathne and D. Ishara, "A Semantic Similarity Measure Based News Posts Validation on Social Media," 2018 3rd International Conference on Information Technology Research (ICITR), 2018, pp. 1-6, doi: 10.1109/ICITR.2018.8736136.