Recently I became interested in P2P messaging and/or protocols. While these protocols can offer security and prevent wiretapping (for example, bitmessaging), there are some serious drawbacks. For one, decentralization is difficult to achieve while maintaining the advantages of a centralized server, which provides major shares of benefits of client-server model. Even if decentralization is achieved, the architectures turns out to be not so well for scalability. I haven’t identified what exactly I am going to work on, but focusing on an aspect that makes the P2P protocols more robust is my motivation behind the project.
It’s a widespread belief that fake news has played a noteworthy roles in shaping the voters pick for the US presidential candidate in the election cycle 2016. Fact checking, and thus weeding out fake news is one of the most difficult challenges that technology can take on; however, it’s unlikely for a set of algorithm to match the accuracy of a human fact checker, as of today. In this paper, we examine how natural language processing can help finding patterns in dubious claim as opposed to stories that are factually consistent. Employing artificial intelligence agent, we are able to show that a “true story” is supported by several sources and report the same event/fact, while a fake news story is likely reported from a single source and gets circulated. In addition to that, we’ll also examine how AI can be used to detect the extent to which a story is verifiable, which is a key characteristic of a credible story.