%0 Conference Paper %1 Diakopoulos:2010:CDP:1753326.1753504 %A Diakopoulos, Nicholas A. %A Shamma, David A. %B Proceedings of the SIGCHI Conference on Human Factors in Computing Systems %C New York, NY, USA %D 2010 %I ACM %K k3 politics sentiment tv twitter %P 1195--1198 %R 10.1145/1753326.1753504 %T Characterizing Debate Performance via Aggregated Twitter Sentiment %U http://doi.acm.org/10.1145/1753326.1753504 %X Television broadcasters are beginning to combine social micro-blogging systems such as Twitter with television to create social video experiences around events. We looked at one such event, the first U.S. presidential debate in 2008, in conjunction with aggregated ratings of message sentiment from Twitter. We begin to develop an analytical methodology and visual representations that could help a journalist or public affairs person better understand the temporal dynamics of sentiment in reaction to the debate video. We demonstrate visuals and metrics that can be used to detect sentiment pulse, anomalies in that pulse, and indications of controversial topics that can be used to inform the design of visual analytic systems for social media events. %@ 978-1-60558-929-9

@inproceedings{Diakopoulos:2010:CDP:1753326.1753504, abstract = {Television broadcasters are beginning to combine social micro-blogging systems such as Twitter with television to create social video experiences around events. We looked at one such event, the first U.S. presidential debate in 2008, in conjunction with aggregated ratings of message sentiment from Twitter. We begin to develop an analytical methodology and visual representations that could help a journalist or public affairs person better understand the temporal dynamics of sentiment in reaction to the debate video. We demonstrate visuals and metrics that can be used to detect sentiment pulse, anomalies in that pulse, and indications of controversial topics that can be used to inform the design of visual analytic systems for social media events.}, acmid = {1753504}, added-at = {2016-02-10T16:14:29.000+0100}, address = {New York, NY, USA}, author = {Diakopoulos, Nicholas A. and Shamma, David A.}, biburl = {https://www.bibsonomy.org/bibtex/29a1f3aeee3c3c40954594228fd82c885/asmelash}, booktitle = {Proceedings of the SIGCHI Conference on Human Factors in Computing Systems}, description = {Characterizing debate performance via aggregated twitter sentiment}, doi = {10.1145/1753326.1753504}, interhash = {d843e08eaaa822a5262abc2fee5c7411}, intrahash = {9a1f3aeee3c3c40954594228fd82c885}, isbn = {978-1-60558-929-9}, keywords = {k3 politics sentiment tv twitter}, location = {Atlanta, Georgia, USA}, numpages = {4}, pages = {1195--1198}, publisher = {ACM}, series = {CHI '10}, timestamp = {2016-02-10T16:14:41.000+0100}, title = {Characterizing Debate Performance via Aggregated Twitter Sentiment}, url = {http://doi.acm.org/10.1145/1753326.1753504}, year = 2010 }