Bots in the Election
4:15:00 PMby Anonymous
In the Kno.e.sis Center at Wright State University, we continue to refine our Twitris technology (Licensed by Cognovi Labs LLC) for collective social intelligence to analyze social media (esp. Twitter) in real time. Kno.e.sis and Cognovi Labs teamed up with the Applied Policy Research Institute (APRI) early in the year and created some tools to monitor the debates. See press coverage on TechCrunch. From the time that we first began following the nominees on Twitter, one thing became clear: Donald Trump was considerably more popular than his competition during the primaries as well as the general election. To be honest, I had never considered the possibility that social bots may have been playing a role in this popularity.
After the conclusion of the first debate all parties who had watched our "Debate Dashboard" were shocked not just by the volume of tweets but by the sentiment and emotion that appeared more positive for Trump than Clinton. When we investigated news from major media outlets, we became more and more concerned that our tool had some serious flaws. Due to articles we had seen discussing the large support Trump had on Twitter, we decided to focus on sentiment. Up until a few days before the election, we continued to update and improve our sentiment analysis algorithm.
Notwithstanding the improvements in precision to our sentiment classifier, we continued to see Trump as the clear leader. As the debates came and went, our data remained consistent. We began an urgent quest for reason. We added gender analysis because media outlets were telling us that women were down on Trump and would be a major force in the election. Our analysis did not show this, despite having 96% precision in determining female and male users. We developed a proprietary process to separate users into left-leaning and right-leaning users. We could even say whether a user was strongly or loosely associated with a particular political party. Unfortunately, analyzing the data based on political association didn't help either. Surprisingly many strongly left leaning users were anti-Hillary, just a bit behind the right-leaning users.
After the second debate, we began to see many articles pop up about social bots. Once we began to look more into the issue, we found many articles from early in the year talking about Trump's "Bot Army" (Trumps' Biggest Lie? The Size of His Twitter Following). We had our aha moment. In that article, there is a reference to The Atlantic's use of a tool called BotOrNot. We decided to attempt to use this or some similar tool during the last debate to remove users with bot accounts and analyze the remaining data.
BotOrNot is a tool developed at the University of Indiana, Bloomington with collaboration from the University of Southern California, Marina Del Rey. Their tool creates over one thousand metrics by looking at the user account and analyzing retweets, hashtags, metadata, etc. The tool performed extremely well in the DARPA Twitter Bot Challenge, having correctly identifying all of the known bots (though it did incorrectly mark some additional users as bots). We were excited to learn that they had made their tool available through an API endpoint and decided to run our tweets through the system to test the speed at which we could process users. Twitris at this point was processing nearly 35 tweets per second for the election analysis alone, and it was clear very quickly that their service would not be able to handle the volume of data we would be consuming.
Though the final presidential debate was only several days away, we still had hope that we would find an answer. We saw Prof. Philip Howard, from the University of Oxford, mention in The Washington Post that they considered any user that tweets more than 50 times in one day a bot. It would be relatively simple to create an index of users and simply increment the count per tweet and then check the index quickly as the tweets roll through. We may have done this if our team had been free at the time. Some were working on bug fixes, others on improving sentiment, and still others were working to fix some infrastructure issues that we were experiencing at the time. Our corpus of tweets for the election campaign was on its way to exceeding 60 million tweets. A robust implementation would have required more time than anyone had to offer.
I suppose that now is a better time than never to define what a "bot" is. Phil Howard says, in that Washington Post article mentioned above, that "A Twitter bot is nothing more than a program that automatically posts messages to Twitter and/or auto-retweets the messages of others". I personally like this definition, but it leaves a little wiggle room where tools like BotOrNot, which focus primarily on the user accounts, are considered. A Twitter user can be a real, living, breathing, human being and still exhibit bot-like tweeting habits. I think that this is the reason that Howard's group settled on the 50-tweets-per-day stat instead of relying on a classifier. A user can tweet for themselves part of the time, but still have some automation process that tweets certain things on their behalf. There are many reasons that someone would do this, to increase their Klout score (imagine a LuLaRoe seller, YouTuber, or Blogger), for example. There are many companies that you can pay to automate this kind of activity for you. Some, like Linkis' "Convey" (more on this later) work by finding influential tweets and tweeting them on your behalf. These tweets are fairly easy to spot because they attach "via @c0nvey" to the end of the original tweet.
In the end, what we did was to develop a system that is able to quickly and accurately weed out tweets that were not authored by humans, even if the user account is owned by an actual human. Let's see how well our system stacks up to the BotOrNot service. We collected all of the bots found over one fifteen minute period and ran them through BotOrNot. 67.16% of the accounts from the tweets we labeled were determined by BotOrNot to be “bot” owned accounts. I was a little disappointed by this, so I took a look at the users that BotOrNot dismissed as human. In the first pass, I decided to apply Howard's 50-tweets-per-day rule. Doing this increased the percentage of accurately labeled bot tweets to 73.88%. One of the screen names found among this group actually contained the word "bot" in it. The most important thing for us at this point was to make sure that we weren’t getting a lot of false positives. We looked at each account one by one; we looked at each tweet in our system, both those classified as "likely bot" and "likely human".
Figure: Real-time labeling of bot and human tweets in Twitris
Many of the users with a low tweet-per-day usage had a combination of the two, and looking at the tweets that were labeled "likely bot", nearly all of them contained "via @c0nvey". Digging a bit further, I found that the Convey service has been accused of tweeting on behalf of unwitting users in the past. Here's a thread from one Twitter user talking about his experience:
So, our system is able to accurately detect automated (bot) tweets despite the fact that the user is not a bot. Our average detection rate also looks reasonable compared to other services. We experience bot traffic at a rate of about a 5% per day in our election campaign, though there are days that get to nearly 8%.
Moving Forward (oh, and Fake News)
Elections come and go. Once they are gone, we seek to apply our findings to future work. Being able to detect bots is great; but, what else can we do with that? Well, post-election we have learned the term “fake news” (with content that was entirely made up, not grounded in truth or reality), something that not too many people were concerned about before: see the Google Trend.
Where does all of this fake news come from? There have been troves of news reports blaming Facebook and Twitter for altering the outcome of the election. Obviously, Facebook and Twitter themselves weren’t creating pro-Trump news (Facebook in particular was accused of killing pro-Trump trends); however, some blame these companies for not alerting people that the news that they were “allowing” to spread was fake. I don’t think that that is fair. These companies are reliant on the fact that they don’t publish news (for more, read this article on Facebook, the “News Feed”, and the Communications Decency Act 230) to avoid lawsuits.
During the evaluation of our bot detection system, we noticed something interesting. A large majority of the bot-labeled tweets contained links to dubious looking news stories. Because of our ability to identify these bot tweets, we can exclude them from analysis when considering a brand-centered campaign (like Samsung during the Note 7 battery “situation”). I think that it is important to note that, despite the elimination of “fake news” and “bot tweets” from our analysis, Trump was always winning. We saw the same thing with the Brexit referendum earlier this year, where Twitris helped us to correctly predict the Brexit outcome before the polls closed. There is clear evidence to support the fact that high tweet volume translates to success (except Bernie Sanders, but there may be, uh-hum, other reasons for that). It seems to me that for bots and “fake news” to have swayed the election, they would have needed to be ready to go as soon as Trump announced that he was running, but what we have seen is that he was always ahead.
We will continue to find new ways to leverage everything that we learned from the 2016 Election. If you want to stay up to date on our analysis, please sign up to receive CognoviLabs’ newsletter at www.CognoviLabs.com or join Kno.e.sis on FB, and while you are there check the other post-election analysis.
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ReplyDeleteThe Kno.e.sis Center at Wright State University, in collaboration with Cognovi Labs and the Applied Policy Research Institute, analyzed social media during the 2016 U.S. presidential election to detect the influence of social bots on Twitter. Despite improving sentiment analysis algorithms, the researchers consistently showed positive sentiment for Donald Trump. They discovered the potential influence of social bots on Twitter and developed a system to quickly and accurately identify automated tweets. The researchers found that bot traffic was about 5-8% per day during the election campaign. They plan to apply their findings to future work, particularly in detecting and mitigating the impact of fake news. The researchers also found that high tweet volume seemed to correlate with electoral success, raising questions about the influence of social media dynamics on political outcomes. abogado de nueva jersey flsa
ReplyDeleteThe use of bots can skew public discourse, influence voter perceptions, and even undermine the trust in electoral systems. Addressing this issue requires a multifaceted approach, encompassing technological solutions, regulatory measures, and heightened public awareness.
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Bots can create the illusion of widespread support for a candidate or a cause by generating fake likes, shares, comments, or followers. Their ability to rapidly disseminate information allows them to influence the political discourse, potentially swaying voter opinions and election outcomes.
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Bots in the Election" is likely an insightful and timely exploration of the increasingly significant role that automated bots play in modern electoral processes. This piece probably delves into how bots are used to shape public opinion, disseminate information, and potentially spread misinformation on various digital platforms. It likely raises critical questions about the impact of these bots on the integrity and fairness of elections, highlighting both the technological sophistication of these tools and the challenges they pose to democratic processes.
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Bots in the Election" provides a chilling exposé on the growing influence of automated accounts in shaping political discourse and election outcomes. Through meticulous research and expert analysis, this documentary sheds light on the clandestine tactics employed by bots to manipulate public opinion, spread disinformation, and sway electoral results. With real-world examples and firsthand accounts, the film underscores the urgent need for greater awareness and regulation to safeguard the integrity of democratic processes. "Bots in the Election" serves as a wake-up call to the pervasive threat posed by artificial intelligence in modern politics, urging viewers to remain vigilant and discerning in the face of digital deception.
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The blog post on "blog.knoesis.org/2016/12/bots-in-election.html" delves into the intriguing topic of bots' role in elections, offering valuable insights into their impact on the electoral process. It explores the phenomenon of automated bots and their potential influence on voter behavior and public opinion. With in-depth analysis and compelling examples, the post sheds light on the complexities of modern-day election campaigns and the challenges posed by technological advancements. It prompts readers to contemplate the implications of bots in shaping political discourse and decision-making. Overall, it's an informative and thought-provoking read for anyone interested in the intersection of technology and democracy.
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An excellent examination of how bots affect political discourse and election results can be found on the KNO.E.SIS Research Blog on Bots in the Election. It is a useful tool for comprehending the effects of digital meddling in contemporary elections because of its data-driven methodology and critical viewpoint. henry county dui lawyer Henry County DUI lawyers are known for their expertise in defending DUI cases, providing tailored legal strategies to minimize penalties. Their local knowledge and commitment to protecting clients' rights make them a dependable choice for those facing DUI charges.
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ReplyDeleteAutomated programs known as "bots" are used in elections to sway public opinion and voter behavior. They can interact with users on social media platforms, amplify messages, and disseminate information. Bots are frequently used to propagate false information, establish echo chambers, or control discourse, even though they can be beneficial for voter education. Ensuring fair and transparent electoral processes requires close monitoring and regulation of bot activity.Sexual Defense Attorney,For those accused of sexual offenses, a sexual defense attorney is essential because they offer knowledgeable legal counsel and defend their rights during the court proceedings. They strive to refute the evidence, lessen the severity of the penalties, and get the best result for their clients.
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ReplyDeleteThe Kno.e.sis Center at Wright State University used technology to analyze social media sentiment around political candidates during the election, focusing on Donald Trump's popularity on Twitter. The team discovered that bots were artificially inflated, amplifying positive messages and polarizing content. This led to a reconsideration of their "Debate Dashboard" tool, which could not fully account for the impact of automated accounts. immigration lawyer in houston Lawyers are bound by a code of ethics that requires them to maintain confidentiality, represent their clients to the best of their ability, and avoid conflicts of interest.
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