And actually, he was totally right since we found this initiative very useful. How did you hear about the SAGE Concept Grants, what made you apply and how did the funding help you bring your idea closer to a fully operational tool or package that researchers can use?Ī fellow computer science researcher stumbled upon the SAGE Concept Grants and thought we might be interested, so he let us know. With respect to Botometer, the techniques included in the DDNA package obtained better fake detection results. The only similar service is Botometer, which is a public bot detection service. To this regard, the DDNA is among the first available tools. Unfortunately, there aren’t many services and tools for assessing the veracity and credibility of online data. What sets you apart from other tools and services in this space? In this way, they give the impression of large discussions and widespread interest in the low-value stocks, in an effort to fool automatic trading algorithms and unaware investors. The bots create a large number of fictitious tweets, where they mention low-value stocks with some high-value ones (e.g., Google, Apple, etc.). In another recent study we used DDNA to uncover large botnets that try to artificially inflate the popularity of low-value stocks traded in US financial markets. Interestingly, all such accounts completely stopped tweeting after the Brexit vote. By applying our techniques to a small subset of tweets containing the #Brexit hashtag, we found several hundreds of bot accounts that were frantically tweeting in the weeks before the vote. One such case was the UK’s 2016 EU membership referendum. Since we started experimenting with DDNA, we found many cases of online discussions tampered by bots and filled with fake content. Do you have any interesting examples or case studies to share? We are confident that social science researchers will find it easy and useful to exploit the cutting-edge algorithms and techniques contained in the DDNA package. Thanks to the funding from the SAGE Concept Grant, we had the opportunity to release our methods in the convenient form of both a Python and R package. Moreover, they are usually designed by technicians for technicians and thus they remain confined within the computer science community. However, the tools for assessing the credibility of online data are still few and far between. Many researchers make use of social media and OSN data for their studies. Why does your package resonate so well with social science researchers? By analyzing groups we thus had more information for our analysis, which ultimately yielded positive results. We followed the intuition that an exceptional amount of similarity between different items could serve as a red flag for automated/forged content. Thus, we switched from analyzing single fake items to analyzing groups of items. In other words, it is becoming increasingly difficult to tell apart single fake news and accounts from credible ones.īecause of this challenge, we wanted to fight the battle against fakes in a more favorable scenario. One of the main challenges in detecting fake information is that online fake content, be it a piece of news or a fake account, are so accurately engineered as to often appear credible, if not investigated thoroughly. Based on this idea, we then applied string mining and bioinformatics algorithms to the study of our “digital DNA” strings, with surprisingly good results! Over the past two years, what were your main challenges and how did you overcome them? Then, we thought that the sequence of actions of an account could be represented with a string of characters, similarly to a string of biological DNA. When we first thought about studying online behaviors, we modeled them as sequences of actions. The methods provided by DDNA analyze similarities in online behaviors. In particular, it is designed for assessing the reliability of accounts and content (e.g., detecting fake and bot accounts) in online social networks (OSNs). The Digital DNA (DDNA) Toolbox is a set of methods developed to support scientists in making sense of online data. What is DDNA and how did you start or come up with the idea?
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