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TruthNest: Verifying eyewitness media from social networks

16 November 2015

We are currently experiencing a social web explosion, which is giving the power of speech back to the citizens who had been practically deprived of this since the gradual explosion of the population that made it impossible for news to travel via the old channel of the ‘word-of-mouth’. The problem is that the scales are now very much different and all the sides have their say in this big bang of gossiping: truth and lies, positive and negative, genuine and fake. Looking at Twitter alone, hundreds of millions of tweets are sent per day by a couple of billion users, with millions of people worldwide debating issues in real-time. Full of breaking news, instant opinion and trending topics, it’s a hive of data waiting to be tapped into. Most data providers have now entered into the analysis of internet news and social media sources. The major issue however, is that although the truth is out there it is very difficult to discover it. News experts (journalists), PR professionals, large enterprises etc. are standing still towards the vast amount of information flowing through the social media, unable to verify the validity of that information, without the use of tremendous Human Resources.

TruthNest provides a sound solution to the problem, by applying innovative technologies for assessing the trustworthiness of information originating in Social Media for accurately discovering, analyzing and verifying the credibility and truthfulness of reported events, news and multimedia content that emerge in social media in near real time.

TruthNest is doing this by applying our ‘triple-C concept', a method that is tackling, with a multidimensional approach, the assessment of the trustworthiness of a post by applying metrics under the following pillars:

  • Contributor – This involves all data relevant to the source of information, such as its history, its reputation, its connections and interactions in the social circle and any other information that can assist in the profiling of any contributor of content.
  • Content – This includes analysis methods that can provide clues about the credibility of linked content (as photos and linked web content) and indicate possible manipulations and fraudulent use.
  • Context – This pillar includes all contextual relations which strengthen or weaken the confidence built around several concepts like cross-checking and suspicious behaviour.

Recently, TruthNest has featured in the following articles: 

For more information please visit the TruthNest Web Site.