~ Archive for Elections ~

Artificial Intelligence, your brain, and other things you cannot trust about politics

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A few days ago the Center for Research on Computation and Society organized a workshop with the provocative title “Six Reasons Fake News is the End of the World as we Know It“. I call it provocative because, whether “fake news” is a new thing or not, has been discussed a lot lately. Not all of us agree on what it is, or how novel it is. Some point out that it is as old as newspapers, others see it as something that mainly appeared last year. Yet others doubt that it is even a phenomenon worth discussing and that, instead of fake news, we should talk instead about specific categories such as false news, misinformation, disinformation, and propaganda.

Accepting the challenge, I gave a talk with an equally provocative, I would like to believe, title:  “Artificial Intelligence, your brain, and other things you cannot trust about politics“. You can follow my talk in the video below, but let me give you a list of the “things” that I discussed in the talk:

what-you-cannot-trusst-about-politics

I hope you find it interesting and do your own thinking about what we can trust when it comes to politics. Importantly, we need to figure out how to solve the problems of online misinformation and propaganda that seem to be all around us these days.

Or, to learn how to live with them, which is what I think will happen.

Replication, Verification and Availability for Big Data

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The next step in the evolution of Social Computing Research: Formal acceptance of credit worthiness by the community of Replication, Verification, and Availability of Big Data.


In his response to my posting on Research Replication in Social Computing, Dr. Bernardo Huberman pointed to his letter to Nature on a related issue: Verification of results. Here I expand to include proposal that I have heard others mention recently.

I totally agree, of course, that “Science is unique in that peer review, publication and replication are essential to its progress.” This is what I also propose above. And he focuses on the need for having accessible data so that people can verify claims. For those who may not have access to his letter, I reproduce the central paragraph here:

“More importantly, we need to recognize that these results will only be meaningful if they are universal, in the sense that many other data sets reveal the same behavior. This actually uncovers a deeper problem. If another set of data does not validate results obtained with private data, how do we know if it is because they are not universal or the authors made a mistake? Moreover, as many practitioners of social network research are starting to discover, many of the results are becoming part of a “cabinet de curiosites” devoid of much generality and hard to falsify.”

Let me add something further, that I heard it mentioned by Noshir Contractor and Steffen Staab at the WebScience Track during the WWW2012 conference, that I think will complement the overall proposal: People who make their data available to others should get credit for that. After all, in Science a lot of time is spend collecting and cleaning data, and whose who do that and make their data available to other researchers for verification, meta-analyses and studying of other research questions should be rewarded for their contributions.

I believe the time is right to introduce formal credit for replication of results on comparable data sets, verification on the same data set, and for making data accessible to others for further and meta-analysis. I plan to use much of my group’s research time on these issues this summer and publish our findings afterwards.

Three Social Theorems

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Dear Readers,

Below are my annotated notes from a talk I gave at Berkman’s Truthiness in Digital Media Symposium a few weeks ago. I introduced the concept of Social Theorems, as a way of formulating the findings of the research that is happening the last few years in the study of Social Media. It is my impression that, while we publish a lot of papers, write a lot of blogs and the journalists report often on this work, we have troubles communicating clearly our findings. I believe that we need both to clarify our findings (thus the Social Theorems), and to repeat experiments so that we know we have enough evidence on what we really find. I am working on a longer version of this blog and your feedback is welcome!

Best,
P. Takis Metaxas

With the development of the Social Web and the availability of data that are produced by humans, Scientists and Mathematicians have gotten an interest in studying issues traditionally interesting mainly to Social Scientists.

What we have also discovered is that Society is very different than Nature.

What do I mean by that? Natural phenomena are amenable to understanding using the scientific method and mathematical tools because they can be reproduced consistently every time. In the so-called STEM disciplines, we discover natural laws and mathematical theorems and keep building on our understanding of Nature. We can create hypotheses, design experiments and study their results, with the expectation that, when we repeat the experiments, the results will be substantially the same.

But when it comes to Social phenomena, we are far less clear about what tools and methods to use. We certainly use the ones we have used in Science, but they do not seem to produce the same concrete understanding that we enjoy with Nature. Humans may not always behave in the same, predictable ways and thus our experiments may not be easily reproducible.

What have we learned so far about Social phenomena from studying the data we collect in the Social Web? Below are three Social Theorems I have encountered in the research areas I am studying. I call them “Social Theorems” because, unlike mathematical Theorems, they are not expected to apply consistently in every situation; they apply most of the time and when enough attention has been paid by enough people. Proving Social Theorems involves providing enough evidence of their validity, along with description of their exceptions (situations that they do not apply). It is also important ti have a theory, an explanation, of why they are true. Disproving them involves showing that a significant number of counter examples exists. It is not enough to have a single counter example to disprove a social theorem, as people are able to create one just for fun. One has to show that at least a significant minority of all cases related to a Social Theorem are counter-examples.

SoThm 1. Re-tweets (unedited) about political issues indicate agreement, reveal communities of likely minded people.

SoThm 2. Given enough time and people’s attention, lies have short questioned lives.

SoThm 3. People with open minds and critical thinking abilities are better at figuring out truth than those without. (Technology can help in the process.)

So, what evidence do we have so far about the validity of these Social Theorems? Since this is simply a blog, I will try to outline the evidence with a couple of examples. I am currently working on a longer version of this blog, and your feedback is greatly appreciated.

Evidence for SoThm1.

There are a couple papers that present evidence that “Re-tweets (unedited) about political issues indicate agreement, reveal communities of likely minded people.” The first is the From Obscurity to Prominence in Minutes: Political Speech and Real-Time Search paper that I co-authored with Eni Mustafaraj and presented at the WebScience 2010 conference. When we looked at the most active 200 Twitter users who were tweeting about the 2010 MA Special Senatorial election (those who sent at least 100 tweets in the week before the elections), we found that their re-tweets were revealing their political affiliations. First, we completely characterized them into liberals and conservatives based on their profiles and their tweets. Then we looked at how they were retweeting. In fact, 99% of the conservatives were only retweeting other conservatives’ messages and 96% of liberals those of other liberals’.

Then we looked at the retweeting patterns of the 1000 most active accounts (those sent at least 30 tweets in the week before the elections) and we discovered the graph below:

As you may have guessed, the liberals and conservatives are re-tweeting mostly the messages of their own folk. In addition, it makes sense: The act of re-tweeting has the effect of spreading a message to your own followers. If a liberal or conservative re-tweets (=repeats a message without modification), he/she wants this message to spread. In a politically charged climate, e.g., before some important elections, he/she will not be willing to spread a message that he disagrees with.

The second evidence comes from the paper “Political Polarization on Twitter” by Conover et. al. presented at the 2011 ICWSM conference. The retweeting pattern, shown below, indicates also a highly polarized environment.

In both cases, the pattern of user behavior is not applying 100% of the time, but it does apply most of the time. That is what makes this a Social Theorem.

Evidence for SoThm2.

The “Given enough time and people’s attention, lies have short questioned lives” Social Theorem describes a more interesting phenomenon because people tend to worry that lies somehow are much more powerful than truths. This worry stems mostly from our wish that no lie ever wins out, though we each know several lies that have survived. (For example, one could claim that there are several major religions in existence today that are propagating major lies.)

In our networked world, things are better, the evidence indicates. The next table comes from the “Twitter Under Crisis: Can we trust what we RT?” paper by Mendoza et. al., presented at the SOMA2010 Meeting. The authors examine some of the false and true rumors circulated after the Chilean earthquake in 2010. What they found is that rumors about confirmed truths had very few “denies” and were not questioned much during their propagation. On the other hand, those about confirmed false rumors were both questioned a lot and were denied much more often (see the last two columns enclosed in red rectangles). Why does this make sense? Large crowds are not easily fooled as the research on crowd sourcing has indicated.

Again, these findings do not claim that no lies will ever propagate, but that they will be confronted, questioned, and denied by others as they propagate. By comparison, truths will have a very different experience in their propagation.

The next evidence comes from the London Riots in August 2011. At the time, members of the UK government accused Twitter of spreading rumors and suggested it should be restricted in crises like these. The team that collected and studied the rumor propagation on Twitter found that this was not the case: False rumors were again, short-lived and often questioned during the riots. In a great interactive tool, the Guardian shows in detail the propagation of 7 such false rumors. I am reproducing below an image of one of them, the interested reader should take a closer look at the Guardian link.

 

 

During the Truthiness symposium, another case was presented, one that supposedly shows the flip side of this social theorem: That “misinformation has longer life, further spread on Twitter than accompanying corrections”. I copy the graph that supposedly shows that, for reference.

Does this mean that the Social Theorem is wrong? Recall that a Social Theorem cannot be refuted by a single counter-example, but by demonstrating that, at least a significant minority of counter examples, exists.

Further, the above example may not be as bad as it looks initially. First, note that the graph shows that the false spreading had a short life, it did not last more than a few hours. Moreover, note that the false rumor’s spreading was curbed as soon as the correction came out (see the red vertical line just before 7:30PM). This indicates that the correction probably had a significant effect in curbing the false information, as it might have continue to spread at the same rate as it did before.

 

Evidence for SoThm3.

I must admit that “People with open minds and critical thinking abilities are better at figuring out truth than those without” is a Social Theorem that I would like to be correct, I believe it to be correct, but I am not sure on how exactly to measure it. It makes sense: After all our educational systems since the Enlightenment is based on it. But how exactly do you created controlled experiments to prove or disprove it?

Here, Dear Reader, I ask for your suggestions.

 

 

Misinformation and Propaganda in Cyberspace

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Dear Readers,

The following is a blog that I wrote recently for a conference on “Truthiness in Digital Media” that is organized by the Berkman Center in March. It summarizes some of the research findings that have shaped my approach to the serious challenges that misinformation propagation poses in Cyberspace.

Do you have examples of misinformation or propaganda that you have seen on the Web or on Social Media? I would love to hear from you.

Takis Metaxas

 


Misinformation and Propaganda in Cyberspace

Since the early days of the discipline, Computer Scientists have always been interested in developing environments that exhibit well-understood and predictable behavior. If a computer system were to behave unpredictably, then we would look into the specifications and we would, in theory, be able to detect what went wrong, fix it, and move on. To this end, the World Wide Web, created by Tim Berners-Lee, was not expected to evolve into a system with unpredictable behavior. After all, the creation of WWW was enabled by three simple ideas: the introduction of the URL, a globally addressable system of files, the HTTP, a very simple communication protocol that allowed a computer to request and receive a file from another computer, and the HTML, a document-description language to simplify the development of documents that are easily readable by non-experts. Why, then, in a few years did we start to see the development of technical papers that included terms such as “propaganda” and “trust“?

Soon after its creation the Web began to grow exponentially because anyone could add to it. Anyone could be an author, without any guarantee of quality. The exponential growth of the Web necessitated the development of search engines (SEs) that gave us the opportunity to locate information fast. They grew so successful that they became the main providers of answers to any question one may have. It does not matter that several million documents may all contain the keywords we were including in our query, a good search engine will give us all the important ones in its top-10 results. We have developed a deep trust in these search results because we have so often found them to be valuable — or, when they are not, we might not notice it.

As SEs became popular and successful, Web spammers appeared. These are entities (people, organizations, businesses) who realized that they could exploit the trust that Web users placed in search engines. They would game the search engines manipulating the quality and relevance metrics so as to force their own content in the ever-important top-10 of a relevant search. The Search Engines noticed this and a battle with the web spammers ensued: For every good idea that search engines introduced to better index and retrieve web documents, the spammers would come up with a trick to exploit the new situation. When the SEs introduced keyword frequency for ranking, the spammers came up with keyword stuffing (lots of repeating keywords to give the impression of high relevance); for web site popularity, they responded with link farms (lots of interlinked sites belonging to the same spammer); in response to the descriptive nature of anchor text they detonated Google bombs (use irrelevant keywords as anchor text to target a web site); and for the famous PageRank, they introduced mutual admiration societies (collaborating spammers exchanging links to increase everyone’s PageRank). In fact, one can describe the evolution of search results ranking technology as a response to Web spamming tricks. And since for each spamming technique there is a corresponding propagandistic one, they became the propagandists of cyberspace.

Around 2004, the first elements of misinformation around elections started to appear, and political advisers recognized that, even though the Web was not a major component of electoral campaigns at the time, it would soon become one. If they could just “persuade” search engines to rank positive articles about their candidates highly, along with negative articles about their opponents, they could convince a few casual Web users that their message was more valid and get their votes. Elections in the US, after all, often depend in a small number of closely contested races.

Search Engines have certainly tried hard to limit the success of spammers, who are seen as exploiting this technology to achieve their goals. Search results were adjusted to be less easily spammable, even if this meant that some results were hand-picked rather than algorithmically produced. In fact, during the 2008 and the 2010 elections, searching  the Web for electoral candidates would yield results that contained official entries first: The candidate’s campaign sites, the office sites, and wikipedia entries topped the results, well above even well-respected news organizations. The embarrassment of being gamed and of the infamous “miserable failure” Google bomb would not be tolerated.

Around the same time we saw the development of the Social Web, networks that allow people connect, exchange ideas, air opinions, and keep up with their friends. The Social Web created opportunities both for spreading political (and other) messages, but also misinformation through spamming. In our research we have seen several examples of propagation of politically-motivated misinformation. During the important 2010 Special Senatorial election in MA, spammers used Twitter in order to create a Google bomb that would bring their own messages to the third position of the top-10 results by frequently repeating the same tweet. They also created the first Twitter bomb targeting individuals interested in the MASEN elections with misinformation about one of the candidates, and created a pre-fab Tweet factory imitating a grass-roots campaign, attacking news organizations and reporters (a technique known as “astroturfing“).

Like propaganda in society, spam will stay with us in cyberspace. And as we increasingly look to the Web for information, it is important that we are able to detect misinformation. Arguably, now is the most important time for such detection, since we do not currently have a system of trusted editors in cyberspace like that which has served us well in the past (newspapers, publishers, institutions). What can we do?

* Retweeting reveals communities of likely-minded people: There are 2 larger groups that naturally arise when one considers the retweeting patterns of those tweeting during the 2010 MA special election. Sampling reveals that the smaller contains liberals and the larger conservatives. The larger one appears to consist of 3 different subgroups.

Some promising research in social media has shown potential in using technology to detect astroturfing. In particular, the following rules hold true most (though not all) of the time:

  1. The credibility of the information you receive is related to the trust you have towards the original sender and to those who retweeted it.
  2. Not only do Twitter friends (those that you follow) reveal a similarly-minded community, their retweeting patterns make these communities stronger and more visible.
  3. While both truths and lies propagate in cyberspace, lies have shorter life-spans and are questioned more often.

While we might prefer an automatic way of detecting misinformation with the use of algorithms, this will not happen. Citizens of cyberspace must become educated about how to detect misinformation, be provided with tools that will help them question and verify information, and draw on the strengths of crowd sourcing through their own groups of trusted editors. This Berkman conference will help us push in this direction.

 

Election time, and the predicting is easy…

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Election time, and the predicting is easy…

As I am sure you have heard, the Iowa caucus results are in. Several journalists are reporting on the elections along with claims of “predictions” that social media are supposedly making. And the day after the Iowa caucus, they are wondering whether Twitter predicted correctly or not. And they look at the “professionals” for advise such as Globalpoint, Sociagility, Socialbackers and other impressive sounding companies.

Shepard Fairey meets Angry Birds: Poster of our 2011 ICWSM submission "Limits of Electoral Predictions using Twitter"

Well, Twitter did not get it right. That is not surprising to my co-authors and I.  Yet, they try to find a silver lining, by claiming smaller predictions such as “anticipating Santorum’s excellent performance than the national polls accomplished.” Of course, the fact that Twitter missed the mismatches with the other 5 candidates is ignored. Why can’t they see that?

A few years ago I had created a questionnaire to help my students sharpen their critical thinking skills. One question that the vast majority got right was the following: “Is Microsoft the most creative tech company?” If one were to do a Web search on this question, the first hit (the “I feel lucky” button) would be Microsoft’s own Web page, because it had as title “Microsoft is the most creative tech company.” My students realized that Microsoft may not be providing an unbiased answer to this question, and ignored it.

It is exactly this critical thinking principle that journalists obsessed with election predictions are getting wrong: The companies I mentioned above ( Globalpoint, Sociagility, Socialbackers ) are all in the business of making money by promising magical abilities in their own predictions and metrics. One should not take their claims on face value because they have financial conflict of interest in giving misleading answers (e.g. “Comparing our study data with polling data from respected independent US political polling firm Public Policy Polling, we discovered a strong, positive correlation between social media performance and voting intention in the Iowa caucus.” Note that even after the elections they talk about intentions, not results.)

That’s not the only example violating this basic critical thinking principle I saw today. Earlier, I had received a tweet that “Americans more susceptible to online scams than believed, study finds“. The article reports that older, rich, highly educated men from the Midwest, politically affiliated with the Green Party are far less susceptible to scam than young, poor, high school dropout women from the Southwest that are supporting Independents. If you read the “study” findings, you will be even more confused about the quality of this study. A closer look reveals that the “study” was done by PC Tools, a company selling “online security and system utility software.” Apparently, neither the vagueness of the “survey” nor the financial conflict of interest of the surveying company raised any flags for the reporter.

In the Web era, information is finding us, not the other way around. Being able to think critically will be crucial.

 

 

Predict the Future!

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The title may seem redundant. Of course if you  are going to predict, you should predict the future — what else, predict the past? But, when referring to social media data it may not be that redundant. In recent years there has been an increase of research on social media data predicting the future, predicting the present, and predicting the past using knowledge acquired in the future.

Why is predicting important? Predicting is equivalent to intelligence, with an important qualification: We admire the intelligence of someone who can predict what is going to happen, but only when they can explain why they are able to do so. If one (e.g., an octopus) is able to predict without explanation, we tend to downgrade it as coincidence.

Earlier today, the Pew Research Center on Journalism published an analysis entitled “Twitter and the Campaign“. They present a detailed study of millions of tweets and blogs, about what people say on social media about the candidates for the 2012 elections. (Not too many nice things, it turns out, except for Ron Paul, who, at the same time, is trailing on the polls.)

So, what does this mean for the predictive power of Twitter? Is he going to win because tweets have good things to say about him, or will he lose because tweets have good things to say about him? (Hint: The answer is “yes”.)

Shepard Fairey meets Angry Birds: Poster of our 2011 ICWSM submission "Limits of Electoral Predictions using Twitter"

Earlier this year, with my colleagues Eni Mustafaraj, Dani Gayo-Avello and student Catherine Lui we studied this question. Can one, analyzing social media data, predict the outcome of the US congressional elections? We did not find encouraging results, in neither the Google Trends data nor the Twitter data — thus the ingenious poster above that Dani designed.

When it comes to something so important as the elections, social media will be manipulated, because the stakes are too high. One should keep that in mind as we get closer to election time and “news articles” will start appearing arguing that someone will win or lose based on the number of friends or followers this candidate has. If the author gets it right, he will make sure to remind us in the future. If he gets it wrong, he will forget it first.

Today's mentally flexible tweet. Why is this important? What is special about the last 24 hours? Who is missing?

This does not mean that nothing can be predicted using social media. Movie sales can be predicted, as Bernando Huberman and his colleague showed. Flu outbreaks and periodic sales can be predicted, too. But not elections. At least without some sophisticated filtering that makes them as representative and competitive to the professional pollsters.

 

 

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