2017-08-27: Media Manipulation research at the Berkman Klein Center at Harvard University Trip Report

A photo of me inside "The Yellow House" -
The Berkman Klein Center for Internet & Society
On June 5, 2017, I started work as an Intern at the Berkman Klein Center for Internet & Society at Harvard University under the supervision of Dr. Rob Faris, the Research Director for the Berkman Klein Center. This was a wonderful opportunity to conduct news media related research, and my second consecutive Summer research at Harvard. The Berkman Klein Center is an interdisciplinary research center that researches the means to tackle some of the biggest challenges on the Internet. Located in a yellow house at the Harvard Law School, the Center is committed to studying the development, dynamics, norms and standards of cyberspace. The center has produced many significant contributions such as the review of ICANN (Internet Corporation for Assigned Names and Numbers) and the founding of the DPLA (Digital Public Library of America).
During the first week of my Internship, I met with Dr. Faris to identify the research I would conduct in collaboration with Media Cloud at Berkman. Media Cloud, is an open-source platform for studying media ecosystems. The Media Cloud platform provides various tools for studying media such as Dashboard, Topic Mapper, and Source Manager
Media Cloud tools for visualizing and analyzing online news
Dashboard helps you see how a specific topic is spoken about in digital media. Topic Mapper helps you conduct topic in-depth analysis by identifying the most influential sources and stories. Source Manager helps explore the Media Cloud vast collection of digital media. The Media Cloud collection consists of about 547 million stories from over 200 countries. Some of the most recent Media Cloud research publications include: "Partisanship, Propaganda, and Disinformation: Online Media and the 2016 U.S. Presidential Election" and "Partisan Right-Wing Websites Shaped Mainstream Press Coverage Before 2016 Election."

Partisanship, Propaganda, and Disinformation: Online Media and the 2016 U.S. Presidential Election | Berkman Klein Center

In this study, we analyze both mainstream and social media coverage of the 2016 United States presidential election. We document that the majority of mainstream media coverage was negative for both candidates, but largely followed Donald Trump's agenda: when reporting on Hillary Clinton, coverage primarily focused on the various scandals related to the Clinton Foundation and emails.

Partisan Right-Wing Websites Shaped Mainstream Press Coverage Before 2016 Election, Berkman Klein Study Finds | Berkman Klein Center

The study found that on the conservative side, more attention was paid to pro-Trump, highly partisan media outlets. On the liberal side, by contrast, the center of gravity was made up largely of long-standing media organizations.

Rob and I narrowed my research area to media manipulation. Given, the widespread concern about the spread of fake news especially during the 2016 US General Elections, we sought to study the various forms of media manipulation and possible measures to mitigate this problem. I worked closely with Jeff Fossett, a co-intern on this project. My research about media manipulation began with a literature review of the state of the art. Jeff Fosset and I explored various research and news publications about media manipulation.


How the Trump-Russia Data Machine Games Google to Fool Americans

A year ago I was part of a digital marketing team at a tech company. We were maybe the fifth largest company in our particular industry, which was drones. But we knew how to game Google, and our site was maxed out.

Roger Sollenberger revealed a different kind of organized misinformation/disinformation campaign from the conventional publication of fake news (pure fiction - fabricated news). This campaign is based on Search Engine Optimization (SEO) of websites. Highly similar less popular (by Alexa rank) and often fringe news websites beat more popular traditional news sites in the Google rankings. They do this by optimizing their content with important trigger keywords, generate a massive volume of similar fresh (constantly updated) content, and link among themselves.

Case 2: Data & Society - Manipulation and Disinformation Online

Media Manipulation and Disinformation Online

Data & Society  introduced the subject of media manipulation in this report: media manipulators such as some far-right groups exploit the media's proclivity for sensationalism and novelty over newsworthiness. They achieve this through the strategic use of social media, memes, and bots to increase the visibility of their ideas through a process known as "attention hacking."

Case 3: Comprop - Computational Propaganda in the United States of America: Manufacturing Consensus Online 

Computational Propaganda in the United States of America: Manufacturing Consensus Online

This report by the Computational Propaganda Project at Oxford University, illustrated the influence of bots during the 2016 US General Elections and dynamics between bots and human users. They illustrated how armies of bots allowed campaigns, candidates, and supporters to achieve two key things during the 2016 election, first, to manufacture consensus and second, to democratize online propaganda.
COMPROP: 2016 US General Election sample graph showing network of humans (black nodes) retweeting bots (green nodes)
Their findings showed that armies of bots were built to follow, retweet, or like a candidate's content making that candidate seem more legitimate, more widely supported, than they actually are. In addition, they showed that the largest botnet in the pro-Trump network was almost 4 times larger than the largest botnet in the pro-Clinton network:
COMPROP: 2016 US General Election sample botnet graph showing the pro-Clinton and more sophisticated pro-Trump botnets

Based on my study, I consider media manipulation as:

calculated efforts taken to circumvent or harness the mainstream media in order to set agendas and propagate ideas, often with the utilization of social media as a vehicle.
On close consideration of the different mechanisms of media manipulation, I saw a common theme. This common theme of media manipulation was described by the Computational Propaganda Project as "Manufactured Consensus." The idea of manufactured consensus is: we naturally lend some credibility to stories we see at multiple different sources, and media manipulators know this. Consequently, some media manipulators manufacture consensus around a story, sometimes pure fabrication (fake news), and some other times, they take some truth from a story, distort it, and replicate this distorted version of the truth across multiple source to manufacture consensus. An example of this manufacture of consensus is Case 1. Manufactured consensus ranges from fringe disinformation websites to bots on Twitter which artificially boost the popularity of a false narrative. The idea of manufactured consensus motivated by attempt to first, provide a means of identifying consensus, second, learn to distinguish organic consensus from manufactured consensus both within news sources and Twitter. I will briefly outline the beginning part of my study to identify consensus specifically across news media sources.
I acknowledge there are multiple ways to define "consensus in news media," so I define consensus within news media:
 as a state in which multiple news sources report on the same or highly similar stories.
Let us use a graph (nodes and edges) to identify consensus in new media networks. In this graph representation, nodes represent news stories from media sources and consensus is captured by edges between the highly similar or the same news stories (nodes). For example, Graph 1 below shows consensus between NPR and BBC  for a story about a shooting in a Moscow court.
Graph 1: Consensus within NPR and BBC for "Shooting at a Moscow Court" story
Consensus may occur within the mainstream left news media (Graph 2) or the mainstream right media (Graph 3). 
Graph 2: Consensus on the left (CNN, NYTimes & WAPO) media for the "Transgender ban" story
Graph 3: Consensus on the right (Breitbart, The blaze, & Fox) for the "Republican senators who killed the Skinny repeal bill" story
Consensus may or may not be exclusive to left, center or right media, but if there is consensus across different media networks (e.g., mainstream left, center and right), we would like to capture or approximate the level of bias or "spin" expressed by the various media networks (left, center and right). For example, on August 8, 2017, during a roundtable in his Golf Club in Bedminster New Jersey, President Trump said if North Korea continues to threaten the US, "they will be met with fire and fury." Not surprisingly, various news media reported this story, in order words there was consensus within left, center and right news media organizations (Graph 4) for the "Trump North Korea fire and fury" story.
Graph 4: Consensus across left, center and right media networks for the "Trump North Korea fire and fury story"
Let us inspect the consensus Graph 4 closely, beginning with the left, then the center and right, consider the following titles:
  1. Calm down: we’re (probably) not about to go to war with North Korea, I’m at least, like, 75 percent sure.
  2. Trump now sounds more North Korea-y than North Korea
Politicus USA:
  1. Trump Threatens War With North Korea While Hanging Out In The Clubhouse Of His Golf Club
Huffington Post, Washington Post, and NYTimes, respectively:
The Hill:
Gateway Pundit, The Daily Caller, Fox, Breitbart, Conservative Tribune:
  1. WOW! North Korea Says It Is ‘Seriously Considering’ Military Strike on Guam (Gateway Pundit)
  2. North Korea: We May Attack Guam (The Daily Caller)
  3. Trump: North Korea 'will be met with fire and fury like the world has never seen' if more threats emerge (Fox)
  4. Donald Trump Warns North Korea: Threats to United States Will Be Met with ‘Fire and Fury’ (Breitbart)
  5. Breaking: Trump Promises Fire And Fury.. Attacks North Korea In Unprecedented Move (Conservative Tribune)
  6. North Korea threatens missile strike on Guam (Washington Examiner)
In my opinion, on the left, consider the critical outlook offered by Vox, claiming the President sounded like the North Korean Dictator Kim Jong Un. At the center, The Hill emphasized the unfavorable response of some senators due to the President's statement. I think one might say the left and some parts of the center painted the President as reckless due to his threats. On the right, consider the focus on the North Korean threat to strike Guam. The choice of words and perspectives reported on this common story exemplifies the "spin" due to the political bias of the various polarized media. We would like to capture this kind of spin. But it is important to note that our goal is NOT to determine what is the truth. Instead, if we can identify consensus and go beyond consensus to capture or approximate spin, this solution could be useful in studying media manipulation. It is also relevant to investigate if spin is related to misinformation or disinformation.
I believe the prerequisite for quantifying spin is identifying consensus. The primitive operation of identifying consensus is the binary operation of measuring the similarity (or distance) between two stories. I have began this analysis with an algorithm in development. This algorithm was applied to generate Graphs 1-4. Explanation of this algorithm is beyond the scope of this post, but you may see the algorithm in action through this polar media consensus graph, which periodically computes a consensus graph for left, center and right media.
Consensus graph generated by an algorithm in development
The second part of our study was to identify consensus on Twitter. I will strive to report the developments of this research as well as a formal introduction of the consensus identifying algorithm when our findings are concrete. 
In addition to researching media manipulation, I had the pleasure to see the 4th of July fireworks across the Charles River from Matt's rooftop, and attend Law and Cyberspace lectures hosted by Harvard Law School Professors - Jonathan Zittrain and Urs Gasser. I had the wonderful opportunity to teach Python and learn from my fellow interns, as well as present my media manipulation research to Harvard LIL.

Media manipulation is only going to evolve, making its study crucial. I am grateful for the constant guidance of my Ph.D supervisors, Dr. Michael Nelson and Dr. Michele Weigle, and am also very grateful to the Dr. Rob Faris at Media Cloud, and the rest of Berkman Klein community for providing me with the opportunity to research this pertinent subject.

-- Nwala (@acnwala)