Published By: Scientific American, 8/18/2016
Summary: Data scientists used post metadata and patterns in post format to show that tweets from @realDonaldTrump come from different people, and sentiment analysis to show that the tweets from Trump himself differ in tone. By identifying patterns in public communications, they found information that was (presumably) not supposed to be public.
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Extended Discussion Questions:
- What important information was revealed here? Is it surprising that campaign staff would “ghost-tweet” for a presidential candidate?
- Is there something the Trump campaign could have done to prevent researchers from making this type of analysis?
- Do you think this type of analysis is beneficial or detrimental to the political process? Why? Would you have felt the same way if a different candidate was involved?
CSP Global Impact Learning Objectives/EKs:
EK 7.1.1C Social media continues to evolve and fosters new ways to communicate.
EK 7.2.1A Machine learning and data mining have enabled innovation in medicine, business, and science.
EK 7.3.1G Privacy and security concerns arise in the development and use of computational systems and artifacts.
EK 7.3.1H Aggregation of information, such as geolocation, cookies, and browsing history, raises privacy and security concerns.
Other CSP Big Ideas:
3 Data and Information
Banner Image: “Network Visualization – Violet – Crop 6”, derivative work by ICSI. New license: CC BY-SA 4.0. Based on “Social Network Analysis Visualization” by Martin Grandjean. Original license: CC BY-SA 3.0
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Tagged: 2 Abstraction, 3 Data & Info, 4 Algorithms, 7.1.1 Interaction and cognition, 7.1.1C Social media, 7.2.1 Impact in other fields, 7.2.1A Data impact, 7.3.1 Benefits and harm, 7.3.1G Privacy, 7.3.1H Data aggregation