International Conference on Information Systems (ICIS), 2018
Like many other industries, the media industry has been profoundly affected by social... more Like many other industries, the media industry has been profoundly affected by social media. The public discuss the news on services such as Twitter, and some celebrity tweets are picked up by journalists and become news stories themselves. Information systems research into information diffusion has largely neglected these cross-platform diffusion patterns. To address this gap, the presented research in progress examines indicators of information spill-over between Twitter and ten German news websites, by detecting references in the form of URLs and mentions on both sides. Furthermore, the paper presents automatic and manual methods to identify two categories of spill-over, reference spill-over and content spill-over. Preliminary findings reveal differences between news outlets in how frequently they are referenced by Twitter users, and in how often they reference Twitter in their own stories.
In the context of events that involve public voting, such as televised competitions or elections,... more In the context of events that involve public voting, such as televised competitions or elections, it has increasingly been recognized that communication data from social media is related to the outcome. Existing studies mainly analyse the number of messages and their sentiment, yet the role of different data collection periods has not been examined sufficiently. We collected Twitter data in 2015 and 2016 to examine the relationship between the audience voting of the Eurovision Song Contest and predictors based on quantity and emotions, and compared the results of using data from before and during the event. We found that the choice of time period greatly affected the results obtained. Data collected prior to the event exhibited a much stronger association with the final ranking than data collected during the event. In addition, the model based on pre-event data in 2015 showed considerable accuracy in predicting the 2016 results, illustrating the usefulness of social media data for predicting the outcomes of events outside social media.
International Conference on Information Systems (ICIS), 2018
Like many other industries, the media industry has been profoundly affected by social... more Like many other industries, the media industry has been profoundly affected by social media. The public discuss the news on services such as Twitter, and some celebrity tweets are picked up by journalists and become news stories themselves. Information systems research into information diffusion has largely neglected these cross-platform diffusion patterns. To address this gap, the presented research in progress examines indicators of information spill-over between Twitter and ten German news websites, by detecting references in the form of URLs and mentions on both sides. Furthermore, the paper presents automatic and manual methods to identify two categories of spill-over, reference spill-over and content spill-over. Preliminary findings reveal differences between news outlets in how frequently they are referenced by Twitter users, and in how often they reference Twitter in their own stories.
In the context of events that involve public voting, such as televised competitions or elections,... more In the context of events that involve public voting, such as televised competitions or elections, it has increasingly been recognized that communication data from social media is related to the outcome. Existing studies mainly analyse the number of messages and their sentiment, yet the role of different data collection periods has not been examined sufficiently. We collected Twitter data in 2015 and 2016 to examine the relationship between the audience voting of the Eurovision Song Contest and predictors based on quantity and emotions, and compared the results of using data from before and during the event. We found that the choice of time period greatly affected the results obtained. Data collected prior to the event exhibited a much stronger association with the final ranking than data collected during the event. In addition, the model based on pre-event data in 2015 showed considerable accuracy in predicting the 2016 results, illustrating the usefulness of social media data for predicting the outcomes of events outside social media.
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