Despite the increasing use of social media platforms for information and data collection, its immoderate nature often leads to the spread of rumors - unverified information. At the same time, the opening of social media platforms offers the opportunity to explore how users share and discuss input, as well as to automatically evaluate the assessment of their verification using techniques and different methodologies. To overcome these problems, this study aims to contribute effectively to the area of rumor verification by scraping the web and verifying the information provided. To this end, a Rumor Tracking System is proposed in which a web scraping technique is used to evaluate the news verification based on various sources and features for news checking. The proposed system automatically detects these sources and features on the websites and verifies the content by consulting an information matching algorithm and completing a truth table of the sources and features provided. A combination of information on all the sources and features gathered through the websites is maintained by the system, which is used to determine whether an article is based on rumor or not.
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