A lot has been written lately about fake news, especially related to recent elections. Blame has mostly been put on tech giants Google and Facebook as they became major sources of news. While traditional newspaper outlets such as the Wall Street Journal or Washington Post curate the information manually and perform due diligence, automatic news feeds either require substantial labor or algorithms for detecting fake news. The result is all of the well-known rage about Google and Facebook for enabling the circulation of such news.
The problem of truthfulness of information goes beyond everyday political, economics, and more general journalism related misinformation. In healthcare, there are many sites providing misinformation, especially those originating and hosted in third world countries. It can be devastating if a person acts on such sources. Another service area very prone for misinformation is finance. An erroneous signal or news can quickly be picked by traders or trading algorithms to drive a stock in a wrong direction. Even marketers are in need of knowing what web sites provide fake news since they do not want to advertise on such sites.
Google and Facebook are well known for their prowess in machine learning and artificial intelligence and they sit on top of troves of data. Yet they have not been able to design a robust truth-checking system. For quite some time, there are available sites – we will call them truthfulness brokers – that provide names of other sites known to dispel fake news. Recently Google announced that it will start marking sites with questionable credibility by their search engine. Based on the announcements their strategy is to cross-check the site in question with those black listed by truthfulness brokers. A few browser plug-ins have also emerged with the same purpose and also the same algorithm for tagging credibility of a site.
For the time being this is probably an admissible solution but it definitely begs for more. First, the solutions rely on third part sites. There is no guarantee that truthfulness brokers provide up-to-date listings. Indeed, they mostly rely on manual work and thus they cannot be up-to-date. In addition, a new site needs to continuously release fake news before it catches attention from truthfulness brokers. Another problem is that the current focus of the brokers is on journalism related fake news. The other aforementioned areas do not have such dedicated truthfulness brokers, thus leaving enormous untapped value.
It is clear that a robust scalable solution to the problem must not exclusively rely on truthfulness brokers but has to internally curate data and develop sophisticated machine learning and artificial intelligence algorithms. Solely focusing on web sites without taking into consideration the actual content exhibited has limitations. Instead algorithms and solutions based on knowledge graphs and fact-checking combined with scores for source reliability must be developed.
The problem of truthfulness of information goes beyond everyday political, economics, and more general journalism related misinformation. In healthcare, there are many sites providing misinformation, especially those originating and hosted in third world countries. It can be devastating if a person acts on such sources. Another service area very prone for misinformation is finance. An erroneous signal or news can quickly be picked by traders or trading algorithms to drive a stock in a wrong direction. Even marketers are in need of knowing what web sites provide fake news since they do not want to advertise on such sites.
Google and Facebook are well known for their prowess in machine learning and artificial intelligence and they sit on top of troves of data. Yet they have not been able to design a robust truth-checking system. For quite some time, there are available sites – we will call them truthfulness brokers – that provide names of other sites known to dispel fake news. Recently Google announced that it will start marking sites with questionable credibility by their search engine. Based on the announcements their strategy is to cross-check the site in question with those black listed by truthfulness brokers. A few browser plug-ins have also emerged with the same purpose and also the same algorithm for tagging credibility of a site.
For the time being this is probably an admissible solution but it definitely begs for more. First, the solutions rely on third part sites. There is no guarantee that truthfulness brokers provide up-to-date listings. Indeed, they mostly rely on manual work and thus they cannot be up-to-date. In addition, a new site needs to continuously release fake news before it catches attention from truthfulness brokers. Another problem is that the current focus of the brokers is on journalism related fake news. The other aforementioned areas do not have such dedicated truthfulness brokers, thus leaving enormous untapped value.
It is clear that a robust scalable solution to the problem must not exclusively rely on truthfulness brokers but has to internally curate data and develop sophisticated machine learning and artificial intelligence algorithms. Solely focusing on web sites without taking into consideration the actual content exhibited has limitations. Instead algorithms and solutions based on knowledge graphs and fact-checking combined with scores for source reliability must be developed.
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