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Showing posts from August, 2016

Why Money, Power and Glory Are Bad Motivators

If all we want is money, power, and glory, the world becomes a sad place. Academic research provides solid evidence that the pursuit of these three things makes collaboration among humans miserable. For instance, it appears that students of management and economics, who make the pursuit of money and power their life's goal, are more greedy even before they start their studies, and that they become even more so over the course of their education.   In behavioral research, first year economics students have been shown to be more likely to free-ride in public goods games: In one experiment , students could deposit money into a public account where it was multiplied and distributed to all participants, or keep their money in a private account, and still participate in the distribution of the public pool.   First year graduate students in economics kept eighty percent of the money for themselves, and only put twenty percent into the public pool, compared to all other participants in

Does Twitter Tell Who Will Be the Next President? Comparing Donald Trump’s and Hillary Clinton’s Twitter Influence

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Over the last year Donald Trump has been doing a brilliant job kindling his initially highly unlikely candidacy as US Presidential applicant. Following the principle that there is no good or bad PR, that any news is good news as long as it is in the news, he has acted as a master provocateur. He has been a genius in hitting the soft spots of US society, constantly provoking increasingly broader parts of society with over 32,000 (and rapidly growing) racist, sexist, and religiously offensive tweets. I was curious to see how his Twitter behavior would compare with the articulations on Twitter by his Democrat competitor for the job, Hillary Clinton. Therefore I used Condor’s Twitter EgoFetcher (thanks, Joao, for coming up with the idea) to collect the most recent 10,000 tweets about each candidate on August 4, 2016 at 10.00AM.   The EgoFetcher works in four steps: in step 1 it takes the last N (for example 10,000) tweets about the search term or Twitter handle (e.g. “Donald Trump”).