Identifying Topic-Related Hyperlinks in the Crowd

The microblogging service Twitter has become one of the most popular sources of real time information. Every second, hundreds of URLs are posted on Twitter.
Due to the maximum tweet length of 140 characters, these URLs are in most cases a shortened version of the original URLs. In contrast to the original URLS, which usually provide some hints on the destination Web site and the specific page, shortened links do not tell the users what to expect behind them. These links might contain relevant information or news regarding a certain topic of interest, but they might just as well be completely irrelevant, or even lead to a malicious or harmful website. In this paper, we present our work towards identifying credible Twitter users for given topics. We achieve this by characterizing the content of the posted URLs to further relate to the expertise of Twitter users.

Authors: Patrick Siehndel, Ricardo Kawase, Eelco Herder, Thomas Risse

PDF: siehndel-iswc2014

 

Haters gonna hate: job-related offenses in Twitter

In this paper, we aim at finding out which users are likely to publicly demonstrate frustration towards their jobs on the microblogging platform Twitter – we will call these users haters. We show that the profiles of haters have specific characteristics in terms of vocabulary and connections. The implications of these findings may be used for the development of an early alert system that can help users to think twice before they post potentially self-harming content.

Authors: Ricardo Kawase, Patrick Siehndel, and Eelco Herder

PDF: kawase-www2014

FireMe! Website: http://fireme.l3s.uni-hannover.de/

Who wants to get fired?

Ricardo Kawase presenting @WEBSCI2013

Microblogging services like Twitter have witnessed a flood of users and short updates (tweets). Although this phenomenon brings new possibilities of communication, it also brings dangerous consequences. From time to time, people post tweets guided by strong emotions. By default, tweets are public and anyone, anywhere can instantly see your updates, creating high exposure and lack of awareness about privacy issues. In many cases, this may lead to consequences that can be harmful to one’s personal and professional life. In this paper, we investigate the posting behavior of people who tweet that they hate their jobs and bosses and their responses to alerts about the potential damage that such a tweet may cause. We show that, in many cases, people are not aware about the dimension of their audience, and once alerted, they often regret what they have publicly said. Our analysis leads us to believe that many users could benefit from a ‘give a second thought before posting’ tool that may save their jobs.

Authors:  Ricardo Kawase, Bernardo Pereira Nunes, Eelco Herder, Wolfgang Nejdl and Marco Antonio Casanova

PDF: kawase-websci2013

FireMe! Website: http://fireme.l3s.uni-hannover.de/

FireMe! tracks Twitter users who want to get fired

From time to time, people post tweets guided by strong emotions. By default, tweets are public and anyone, anywhere can instantly see your updates. This may lead to consequences that can be harmful to one’s personal and professional life.

FireMe! monitors Twitter and collects tweets from people who supposedly hate their jobs, or want to kill their bosses or coworkers. These tweets are displayed on the FireMe! site. In addition, FireMe! analyses the sentiment of the tweets, in order to calculate a user’s ’firemeter’ score. The FireMe! leaderboard features those users with the highest scores and who are most likely to get fired.

In a short period of three weeks, FireMe! sent out 4304 tongue-in-cheek warnings. 249 people deleted their questionable tweet within two hours. Further analysis of the tweets showed that job-haters usually have a small number of followers and use more profane language.

FireMe! is developed by Ricardo Kawase and colleagues at the L3S Research Center in Hannover, Germany. The work will be presented at the Web Science conference in Paris in April, 2013.

The team believes that young or inexperienced users would certainly benefit from post-hoc privacy alerts and warnings like FireMe! “Potential dangers of personal, negatively loaded tweets remain abstract for most users, until the damage has been done”.

FireMe! (fireme.l3s.uni-hannover.de)

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Referral paper: Ricardo Kawase, Bernardo Nunes, Eelco Herder, Wolfgang Nejdl and Marco Antonio Casanova. Who Wants To Get Fired? Proceedings ACM Web Science Conference, Web Science 2013, WebSci’13, Paris, France, May 2-4, 2012.
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In the media:

Are you about to get fired because of your Twitter account?
www.dailymail.co.uk/news/article-2300011/Are-fired-Twitter-account-This-new-app-warn-advance.html

FireMe! App Tracks Boss-Hate On Twitter
www.huffingtonpost.com/2013/03/26/fireme-twitter-app_n_2955641.html

FireMe! Twitter alert says you’ve overstepped the mark
www.newscientist.com/blogs/onepercent/2013/03/fireme-twitter-alert.html

Will That Tweet Get You Fired? This App Warns You
mashable.com/2013/03/26/fire-me-app-twitter/

FireMe! Twitter Service Makes Getting Fired Way Easier
www.geekosystem.com/fire-me-twitter/
Video coverage:

krdo: FireMe! App flags fireable tweets
www.krdo.com/news/FireMe-App-flags-fireable-tweets/-/417220/19568398/-/g52t4lz/-/index.html

CNN: Twitter tool saves you from yourself
edition.cnn.com/video/?/video/tech/2013/03/28/exp-twitter-tool-amanpour.cnn

FireMe! App Shows Tweets Trash Talking Bosses
www.newsy.com/videos/fireme-app-shows-tweets-trash-talking-bosses/

Exploiting Twitter as a Social Channel for Human Computation

To fully leverage the innate problem solving capabilities of humans necessitates paradigm shifts towards decentralization of human computation
systems, making the existence of central authorities super uous and even impossible. In this position paper, we propose a novel decentralized
architecture that exploits the Twitter social network as a communication channel for harnessing human computation. Our framework provides
individuals and organizations the necessary infrastructure for human computation, facilitating human task submission, assignment and
aggregation. We presented a proof of concept and explore the feasibility of our approach in the light of several use cases.

Venue: WWW2012, CrowdSearch2012

Authors: Ernesto Diaz-Aviles, Ricardo Kawase, Wolfgang Nejdl

PDF: diaz-www12-CrowdSearch12