I am not sure you can call it "significant" or "serious", but have a look at these two papers:

Rubin et al. "Fake News or Truth? Using Satirical Cues to Detect Potentially Misleading News"

http://www.aclweb.org/anthology/W/W16/W16-0802.pdf

and

"This Just In: Fake News Packs a Lot in Title, Uses Simpler, Repetitive Content in Text Body, More Similar to Satire than Real News" by Horne and Adali

https://arxiv.org/abs/1703.09398

Thank you, Rafal Rzepka, for the second one, I haven't seen that one. The first one I'm intimately familiar with.

I'm also looking for initiatives like the Fake News Challenge from Carnegie Mellon University by Dean Pomerleau: http://www.fakenewschallenge.org/#fn:1

Interesting challenge! 

This could be of potential interest (still haven't read it thoroughly).

"Wikipedia Verification Check: A Chrome Browser Extension", presented at WWW this month.

http://dl.acm.org/citation.cfm?id=3053364

We know that Trump (and Trumpists) are among the most gullible and eager consumers of "fake news" (and they are in the vanguard of those who will attach that pejorative label to anything with which they do not agree).

Following is the introduction of an article that provides a cogent analysis of one recent "real" fake-news account (you can read the remainder of the article at the link):

"Pathology of a Fake News Story

How a neutral piece of coverage on the Syrian war is reframed and distributed to misinform the public— a step by step explanation.

The accuracy of our media has been placed under constant question, with many claims being called out as false, or fake. Sometimes, this is done because someone doesn’t like a story or fact in the media; other times, the news really is fake.

    pathology n.: the study of diseases and of the changes that they       cause -Merriam

This post shows a story originating in the Middle East, about Russian soldiers clearing up bombs left in Syria by Obama’s troops. The story was related using first-hand video and personal accounts, and was picked up by major outlets. However, the truth was that this story was completely false — fabricated and framed in such a way that it looked like real news. We’ll pull on threads behind this fake news, and find just one small part of what may well be a large, international network that is feeding our Western media.

I’ve been working on rumors, fake news and so on for a long time. In 2012, we proposed a multinational project on truth in online media, Pheme, which was funded with €4.3M by the European Commission. The project lasted a little over three years, finishing Spring 2017. We spent the time building definitions of fake news, models of rumors, and tools for picking up untrue claims (a tough project!). We even ran an evaluation where teams from all over the world tried to pick out fake stories from real ones, RumorEval. This was perfect timing, with 2016 being the explosive year that it was and “fake news” becoming a hot term just as our project reached its peak.

In the project, we take in huge amounts of data from the web, streaming in various social media sites, news sites, and so on. These need to be linked and grouped so that our journalists (from Swissinfo.ch) can readily digest content and see what claims and stories are emerging. This grouping and linking is tough, and to understand how to do it, you have to look at the data really close up. And when you do that, you start to see recurring patterns and behaviors in the tweets and stories on the web.

Let’s start with one story ..."

https://medium.com/thoughts-on-journalism/pathology-of-a-fake-news-story-aa572e6764e8

You may find value in reviewing the articles by Derczynski at the link [he is one of the most promising new scholars / researchers / writers in your topic of interest] ... also:

Derczynski, Leon, et al. "Analysis of named entity recognition and linking for tweets." Information Processing & Management 51.2 (2015): 32-49.

https://medium.com/@LeonDerczynski/

Though you might already know, I like to mention "Fake News Challenge," a grassroots effort of over 100 volunteers and 71 teams from academia and industry around the world. Their goals is to address the problem of fake news by organizing a competition to foster development of tools to help human fact checkers identify hoaxes and deliberate misinformation in news stories using machine learning, natural language processing and artificial intelligence. See the following link:

http://www.fakenewschallenge.org

Dear Victoria

Social media ( facebook, Twiter ... ) represents fertile area to spread the fake news because there is no need for references and reviewers, and we can amplify the events in any direction we need and the target of the fake news is controlling the ideas of simple people who will not search for the accuracy of this news, please, read in the following link:

https://www.pri.org/stories/2016-11-26/whats-role-social-media-news-media

Good Luck

Thanks for your response, Bob Skiles. I have come across the Pheme project in looking for work specifically on rumor debunking. I (and my team) will gladly read the reminder of the article and inform ourselves of your current/latest models and tools. The Pheme project was mentioned several times at HICSS 2015 and NAACL 2016 Credibility and Deception Detection Workshops, noting its importance for the deception detection field much prior to the wave of interest to the "fake news". Our deception research also dates back to around 2010, with some very early work on credibility of blogs and certainty identification in 2006.

Khalid Hassan, thank you for your link as well.

Tabata-san, arigatoo gozaimashita. I'm very glad to see that Osaka Osaka Prefecture University colleagues deem the Challenge important.  In 2014 I wrote a short invited piece for the ACM Transactions on Asian Language Information Processing. I was concerned that deception detection data was primarily trained on English (and few other European languages). I wondered about how the phenomenon of lying itself would map onto, say, Japanese cultural perceptions.

https://www.researchgate.net/publication/263732642_Pragmatic_and_Cultural_Considerations_for_Deception_Detection_in_Asian_Languages

I may be wrong but it seems that at this point no culture could escape some effects of the practices of viral digital deception (a.k.a. loosely labelled "fake news").

Article Pragmatic and Cultural Considerations for Deception Detectio...

Benjamin Fabian, thank you. It would be nice to see your survey article once it's out. Please let me know when you have a final product (or a working draft).

I recently started thinking about focusing my PhD research on fake news detection. I made that decision after seeing a Czech documentary movie called "Co dokáže lež" - loosely translated as What is a lie capable of. Unfortunately, all the material is only in Czech and I haven't found any English subtitles.

https://www.csfd.cz/film/511843-co-dokaze-lez/prehled/ - This is a brief summary of the document on our national imdb.com alternative.

http://www.ceskatelevize.cz/ivysilani/11238050887-co-dokaze-lez - This is a link to the actual document that is available online. Unfortunately, I am not sure if it is accessible outside of Czech Republic.

The document mainly talks about a pro-russian propaganda and the means of how it is being spread. The authors do not talk about a technical solution or even about an existence of it. Their research is built interviewing people that are interested in the topic.

I am actually working on building an automated Fakenews detection module using ML and linguistic approaches.

Patrik Drhlík, thank you for the mention of the documentary. Political propaganda is certainly an interesting application of deceptive strategies. The problem there for automated deception is that you need a "gold standard" of truthful texts to contrast them with deceptive ones. What's propaganda and what's not may in fact depend on political biases - if the speaker "buys into" (or believes) what he or she says, he or she may no longer have markers of deception that can be easily identified. Having said that, it is an interesting problem. It's just that interpersonal deception theory may not apply in this case. Some other accurate theoretical and psychologically validated framework is needed prior to attempts to automate any detection. The other problem, so you know it's propaganda, then what? There is no shortage of people calling out propaganda, but simply "exposing it" doesn't seem to make much difference nowadays, does it?

Pascal Lauria, could you please elaborate on your methods. Perhaps a work that you've presented or published would be of most help. Meanwhile, University of Michigan team, headed by Rada Mihalcea, just came out with their own work you may be interested in:

[PDF] Automatic Detection of Fake News

V Pérez-Rosas, B Kleinberg, A Lefevre, R Mihalcea

https://arxiv.org/pdf/1708.07104.pdf

arXiv preprint arXiv:1708.07104, 2017

Abstract: The proliferation of misleading information in everyday access media outlets such as social media feeds, news blogs, and online newspapers have made it challenging to identify trustworthy news sources, thus increasing the need for computational tools able to provide insights into the reliability of online content. In this paper, we focus on the automatic identification of fake content in online news. Our contribution is twofold. First, we introduce two novel datasets for the task of fake news detection, covering seven different news domains. We describe the collection, annotation, and validation process in detail and present several exploratory analysis on the identification of linguistic differences in fake and legitimate news content. Second, we conduct a set of learning experiments to build accurate fake news detectors. In addition, we provide comparative analyses of the automatic and manual identification of fake news.

Here is a list of work that addresses Fake content on Social Networks:

Gupta, A., Kumaraguru, P., Castillo, C., and Meier, P. TweetCred: A Real-time Web-based System for Assessing Credibility of Content on Twitter. Accepted at the 6th International Conference on Social Informatics (SocInfo), 2014. http://precog.iiitd.edu.in/Publications_files/socinfo_paper_102.pdf

Gupta, A., Lamba, H., and Kumaraguru, P. $1.00 per RT #BostonMarathon #PrayForBoston: Analyzing Fake Content on Twitter. IEEE APWG eCrime Research Summit (eCRS), 2013. http://precog.iiitd.edu.in/Publications_files/ecrs2013_ag_hl_pk.pdf

Gupta, A., Lamba, H., Kumaraguru, P., and Joshi, A. Faking Sandy: Characterizing and Identifying Fake Images on Twitter during Hurricane Sandy. Accepted at the 2nd International Workshop on Privacy and Security in Online Social Media (PSOSM), in conjunction with the 22th International World Wide Web Conference (WWW) (2013). http://precog.iiitd.edu.in/Publications_files/9psosm4-gupta.pdf

Dewan, P., Kumaraguru, P. Facebook Inspector (FbI): Towards Automatic Real Time Detection of Malicious Content on Facebook. Accepted at Journal of Social Network Analysis and Mining (SNAM), 2017. Volume 7, Issue 1. http://precog.iiitd.edu.in/Publications_files/FbI_Dewan_SNAM_2017.pdf

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