class: center, middle, inverse, title-slide .title[ # Emotions in pagers after 9/11 ] .subtitle[ ## A Social Data Science story ] .author[ ### David Garcia <br><br> <em>ETH Zurich</em> ] .date[ ### Social Data Science ] --- layout: true <div class="my-footer"><span>David Garcia - Social Data Science - ETH Zurich</span></div> --- # The digital traces of pagers .center[] Back in the 90s, [pagers](https://en.wikipedia.org/wiki/Pager) were a common form of mobile communication. To send a message to a pager, you could call a special phone number, say your message, and the text of the message would appear in the screen of the pager. --- ### Emotions in pagers after 9/11 .center[] --- ## Not so angry americans More than a third of anger words appeared in messages like these:  "Reboot NT machine [name] in cabinet [name] at [location]:CRITICAL:[date and time]." The word "critical" is contained in the anger word list of LIWC! --- ### Anger timeline without REBOOT messages .center[] --- ## The issue of machine-generated traces <div style="float:right"> <img src="https://botometer.osome.iu.edu/static/assets/img/banner-logo.png" alt="Botometer" width="400px"/> </div> Not all digital traces are generated by humans, a large volume of data is generated by machines. During the summer of 2018, Twitter made a big bot cleanse, but independent estimates before reported that between [9% and 15% of Twitter accounts were likely to be bots](https://ojs.aaai.org/index.php/ICWSM/article/view/14871/14721). One of the most widely used methods to detect bots on Twitter is [Botometer](https://botometer.osome.iu.edu/), which is in constant development by the [OSoMe lab at Indiana University](https://osome.iu.edu/). Even if you clean bots from your data, you should always take a good look at your text. You can make word clouds, word shift graphs, or just browse through it to see if you notice anomalous patterns. To sum up: > Take home message: Do not just analyze text, also look at it! --- .center[]