Wednesday, March 21, 2018

Did Cambridge Analytica’s Facebook Harvesting Decide the US Elections?

Have the Russians and Cambridge Analytica been abusing Facebook to influence the US and British elections by spreading fake news to receptive users?
Absolutely Yes!

Has this abuse had an impact on the US elections and Brexit?
Most likely not!

It becomes increasingly clear that people like Donald Trump (through his former confidant Steve Bannon) and Vladimir Putin have been (illegally) harvesting user profiles on Facebook and setting up Twitter bots to spread fakenews on Facebook and Twitter.
Facebook by its own account admitted that fake news produced by the Russians and US Alt-right proponents might have been seen by millions of users. What has not been shown, however, is what effect this has had on the actual voting behavior.

I argue that this effect has been minimal, most likely smaller than the voting margin for Trump in the US elections, and for Brexit in the UK.

While a group of researchers has done an thorough analysis of the dynamics of the spread of fake news on Twitter, they are not measuring what influence this has had on the actual voting behavior. While they found that fake news spread faster than true news, they ignore three key issues:
  1. Echo chambers: the political spectrum has become Balkanized, with people only consuming news they believe in. This effect is reinforced through fake news Twitter bots flooding the feeds of alt-right believers with fake news.
  2. The personality of Twitter users: it has been shown that Twitter users are more extrovert and neurotic than the average population, which also happen to be personality characteristics of alt-right folks.
  3. Twitter bots: today bots have become so sophisticated that it is hard to distinguish them from real people. Fake news spreaders have become adept at using them to game Twitter metrics so that their tweets score high on Twitter.

I argue that the impact of this rumormongering is vastly overrated. I think we should regard the average reader on Twitter and Facebook smarter than unquestioningly passing on whatever rumor is being served up. In my view, these messages full of half-truths and fake news supportive of Brexit and Donald Trump were mostly passed on by followers and believers of Brexit and Trump who had made up their minds well before reading these fake news items, and did not need any influencing.

A second argument comes from the harvesting of user profiles on Facebook, which enables fake news spreaders to identify user personality characteristics based on an approach our team has pioneered a long time ago, which has been also used by a group of other researchers, implemented on a large scale by another team who passed it on to Cambridge Analytica. I don’t think that knowing the personality characteristics of a Facebook user will make her/him so much more receptive to fake news – again people are smarter than that: either they have made up their mind before, or they are not likely to change their opinion because they are more Open, Conscientious, Extrovert, Agreeable, or Neurotic (this are the characteristics measured by the OCEAN personality test researchers commonly use for this type of analysis).

The best approach for fact-checking is a critical, well-informed mind. Our transparency engine offers a powerful tool to support the critical mind.

Saturday, February 10, 2018

Human-computer symbiosis, or computers taking over?

In the earlier days of computer science (1990ties), when I was a post-doc at MIT, there was huge discussion among AI researchers, with people like Marvin Minsky on one side, who said that computers eventually would become smarter than humans, and the majority agreeing that this would never be possible. Fast-forward 25 years, and there is no question that Marvin Minsky was right.

Researchers today envision one of three possible scenarios: the first, least fearsome one, that the human is telling the machine what to do, secondly, we have true human-machine symbiosis with computer and human being equal partners on a task, or third, the machine telling the human what to do. Looking at the current stock market fluctuations, it unfortunately seems we already firmly reached the third scenario, with computers taking over. When after multiyear growth and a record high of the Dow on January 26 2018, a few days later on February 4, the Dow lost 1175 points in a single day, this was its biggest point loss ever, all thanks to automated computer trading. A large part of these fluctuations originated in algorithms tied to exchange traded index funds (E.T.F.), as these are responsible for up to 38% of all trading.  While the impact of these fluctuations can be enormous for the individual investor and companies tied to these trades, there are no worries for the firms trading in these E.T.F.’s, while their robots are doing all their trading automatically.  According to one of the operators of such an investment firm, ”if the market goes down 4 percent, I don’t even care. That is the beauty of systems. Once you build it, there is literally nothing left to do.”

Humans might be tearing their hair out - the psychological fear index VIX beginning of February 2018 rose to multiyear highs – while robots calmly keep on doing their work. This means it is not the shrewd investor anymore who is beating the market, but it is computers equipped with the latest artificial intelligence algorithms.

In earlier research studying crowd behavior on Twitter, we observed a similar pattern, with Twitter bots and humans interacting in a tightly woven dialogue of tweets and retweets, where it becomes nearly impossible to distinguish between humans and robots. More research on untangling the madness of the crowd from the wisdom of the swarm is sorely needed, knowing that both the wisdom and the madness today comes not only from humans, but in increasingly larger parts from the interaction between AI algorithms simulating and beating the human brain and real human brains.
I therefore propose the following (open-ended) research agenda:
  1.  Study the automated robot-controlled feedback loop of the predictive capabilities of online social media on stock prices. This will help develop an algorithmic framework capable of dealing with the impact of artificial intelligence on collective (human) intelligence.
  2. Study the evolution of digital tribes: automatic filtering done by social media companies to preselect the content users get to see on their platforms uses similar techniques as (1). As this preselection is done by automated algorithms in combination with human preferences, it is often argued that it creates information bubbles in which individuals only choose to see content that confirms their world view and no content that opposes it. This leads to the formation of tribes. For instance, we might track the fragmentation of humans co-located in nation states into different digital tribes, such as patriots (fatherlanders), technocrats(nerds), environmentalist (treehuggers), and spiritualists. 
  3. Study the dissemination and evolution of fake news, which reflect the alternative realities of different information bubbles, viewed through the lens of the different tribes.
  4. Study human emotions through sensing of "honest signals" through smart sensors. To enable computers to collaborate with humans on a human-computer symbiotic level, computers will need to get an understanding of the feedback loop between human emotions and computer algorithms.  The goal is to develop alternative means for measuring individual emotions directly from the source – humans – using body sensing systems such as smartwatches and smartphones to track for instance individual happiness, fear and stress to get a baseline system for calibration with online social media.
This list is just a start, your ideas are most welcome?

Thursday, November 09, 2017

What Is the Influence of Your Tweets?

What is the real-world impact of Twitter? It seems that one well-placed tweet can #makeAmericaGreatAgain – at least it got Donald Trump elected as US President, or did it?  One year after his election, even the Russian press says Trump did not “Make America Great Again”.

In this post I would like to explore if Twitter really can help an aspiring leader to change the world, promote a cause, or further his or her own career. I will start by looking at the Twitter behavior of the World’s most famous people, and then identify some characteristics of the most active or prominent twitterers.

In a project with GDI Switzerland we have used prominence in Wikipedia to identify the most influential people in the (English speaking) world. Wikipedia’s notability criteria are a robust mechanism to filter out the most notable 800,000 people. We have then constructed the network among these 800,000 people by linking two people, if their Wikipedia pages are mutually linked. (We call this a bi-directional link). The more central somebody in this network is by number of links, and the more popular her/his friends – in this case the people pages she/he is linked to – the more important that person is. The list below shows the most influential people on the world by this metric, which we call “reach-2”, i.e. how many people can one reach in two degrees of separation.

Num tweets
barack obama
george bush
donald j. trump
bill clinton
Queen Elizabeth II
hillary clinton
Bob dylan
vladimir putin
elton john
Steven Spielberg

The top nine most influential people are all current or former heads of state, plus two world-famous musicians, the tenth does not tweet. Those nine influential leaders however have widely differing numbers of followers. While Queen Elizabeth only has 50,000 followers, Barack Obama not only leads the field with 96 million followers, he also has double the number of followers than his Twitter-trigger-happy successor as US President.
The list of the most followed Twitterers is quite different from the Wikipedia list of World influencers. The top 20 most followed Twitterers are listed below:

Num Followers
katy perry
justin bieber
barack obama
taylor swift
ellen degeneres
lady gaga
justin timberlake
cristiano ronaldo
kim kardashian
britney spears
ariana grande
selena gomez
demi lovato
jimmy fallon
jennifer lopez
donald j. trump
bill gates
oprah winfrey

The empress and emperor of the Twitter universe are singers and actors Katy Perry and Justin Bieber, followed by former politician Barack Obama. With the exception of Barack Obama and Donald Trump, all others of the most followed Twitterers are musicians and actors. There is just one entrepreneur among the top 20 most followed Twitterers, Bill Gates, who rounds up the top-20 list together with Oprah Winfrey.

Counting the professions on the list of the top 30,000 twitterers by reach-2 confirms the leading position of actors and musicians. 

Number Profiles
football player

It seems that the world of Twitter is a world of appearance, and not of substance. Glory beats power, at least on Twitter, actors, musicians, athletes and singers come before politicians. People with real impact, and diligent workers behind the scene, like entrepreneurs, teachers, engineers, scientists, and researchers are totally missing on this top eight list of most popular Twitter professions. Note that this is based on the self-description of people, and it seems we prefer to be seen as actors and models to being perceived as linguists, theoretical physicists or skateboarders (there are 9 of each on our list).

We also looked at the correlations between the reach-2 Wikipedia rank and the Twitter scores among the top most 100,000 influential people on Wikipedia. There is a moderate correlation of 0.3 between Wikipedia reach-2 score and followers on Twitter, more influential people not surprisingly tend to have more followers, but there are prominent exceptions such as Queen Elizabeth and Bob Dylan who have comparatively few followers on Twitter. There is zero correlation however between the number of Tweets of a person and her/his real-world influence measured through reach-2. The conclusion is: You can tweet as much as you want, it will not make you more influential!

There is one significant correlation however of 0.5 between the number of times your tweets have been liked, and the number of your Twitter friends – Twitter friends are the people whom you follow. So it seems die-hard Twitter users and addicts with many Twitter friends are nice to each other, and read and “like” each other’s tweets, which seems to indicate the existence of a Twitter “echo chamber”.

To further understand the characteristics of different Twitter profiles, we looked at some leaders in different categories. The most active Twitterer in our analysis was a UK photographer, video artist, and poet who has tweeted 1,5 million times, this means she sends on average 42 tweets per hour. She posts original poems and pictures, she is also the 12th most liked Twitterer in our analysis, this means others like her tweets a lot. This way she has amassed 1.7 Million followers.
The second most active Twitterer in our analysis is clickbait, that means a Twitter bot which is trying to lure readers to a web site loaded with ads; it has spewed out 1.5 million tweets along the way.  The same is true for the forth most active Twitter user, who is masquerading as a radio station, but in truth is also click-bait trying to collect click-throughs for ad money. The third-most active Twitterer in our sample is the BT help desk who has been tweeting 1.46 million tweets of customer advice, leading to at least 127,000 followers. 

Another typical profile among the "a few hundred thousand tweets cohort" are self-promoting marketers, who follow a few thousand people, with the same number of people following them back, leading to an echo chamber of Twitter noise. These people obsessively check their followers, and also tend to like each others’ tweets, resulting in a mirage of Twitter popularity.

Looking at the ranking by Twitter “likes”, the tweets by the music band “icarusaccount”  garnered the most likes, with 226,000 followers. They are posting original poetic lyrics, about 8000 of them, which amassed over 1.9 million likes. Number two on our list, however, seems to be of the self-promoting marketer category, an Indian who has (re)tweeted 800,000 times leading to over 900,000 likes, mostly retweets about Indian politics. Similarly number three, a Japanese with over 500,000 tweets, mostly retweets of baseball pictures and movies, which got almost 900,000 likes, most likely coming from a small echo chamber. Thus, it seems while there are a few genuine content creators, like the most active video artist and poet, and the most liked music band, a large part of the Twitter users, be it bots or real people, is tweeting into an echo chamber, never to be read, or if at all, by a small group of likeminded individuals.

So I think the conclusion is that Twitter can help you spread your ideas, but it will not convince anybody to accept your ideas! Opinions are made on other media, be it YouTube videos, newspaper articles, or more elaborate blog posts – like this one!

If you want to know about your Twitter influence, try our

Many thanks to my colleague Joao Marcos de Oliveira  for collecting the underlying data for this analysis.