crowdcoding, a method that outsources "coding" tasks to numerous people on the internet, has emerged as a popular approach for annotating texts and visuals. However, the performance of this approach for anal...
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crowdcoding, a method that outsources "coding" tasks to numerous people on the internet, has emerged as a popular approach for annotating texts and visuals. However, the performance of this approach for analyzing social media data in the context of journalism and mass communication research has not been systematically assessed. This study evaluated the validity and efficiency of crowdcoding based on the analysis of 4,000 tweets about the 2016 U.S. presidential election. The results show that compared with the traditional quantitative content analysis, crowdcoding yielded comparably valid results and was superior in efficiency, but was more expensive under most circumstances.
Sentiment is important in studies of news values, public opinion, negative campaigning or political polarization and an explosive expansion of digital textual data and fast progress in automated text analysis provide ...
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Sentiment is important in studies of news values, public opinion, negative campaigning or political polarization and an explosive expansion of digital textual data and fast progress in automated text analysis provide vast opportunities for innovative social science research. Unfortunately, tools currently available for automated sentiment analysis are mostly restricted to English texts and require considerable contextual adaption to produce valid results. We present a procedure for collecting fine-grained sentiment scores through crowdcoding to build a negative sentiment dictionary in a language and for a domain of choice. The dictionary enables the analysis of large text corpora that resource-intensive hand-coding struggles to cope with. We calculate the tonality of sentences from dictionary words and we validate these estimates with results from manual coding. The results show that the crowdbased dictionary provides efficient and valid measurement of sentiment. Empirical examples illustrate its use by analyzing the tonality of party statements and media reports.
crowdcoding is a programming model that outsources a software project implementation to the crowd. As educators, we think that crowdcoding could be leveraged as part of the learning path of engineering students from a...
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crowdcoding is a programming model that outsources a software project implementation to the crowd. As educators, we think that crowdcoding could be leveraged as part of the learning path of engineering students from a computer programming introductory course to solve local community problems. The benefits are twofold: on the one hand the students practice the concepts learned in class and, on the other hand, they participate in real-life problems. Nevertheless, several challenges arise when developing a crowdcoding platform, the first one being how to check the correctness of student's code without giving an extra burden to the professors in the course. To overcome this issue, we propose a novel system that does not resort to expert review;neither requires knowing the right answers beforehand. The proposed scheme automatically clusters the student's codes based solely on the output they produce. Our initial results show that the largest cluster contains the same codes selected as correct by the automated and human testing, as long as some conditions apply.
In democracies with multi-party competition, government parties face a dual challenge in election campaigns: on the one hand, they have to compete against and criticize their coalition partners. On the other hand, the...
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In democracies with multi-party competition, government parties face a dual challenge in election campaigns: on the one hand, they have to compete against and criticize their coalition partners. On the other hand, they should avoid virulent attacks on their partners to preserve their chances of future collaboration in government. Going beyond a dichotomous operationalization of negative campaigning, this manuscript analyses the tonality and volume of negative campaigning. Studying 3030 party press releases in four national Austrian election campaigns, different patterns for the tonality and frequency of negative campaigning reflect the electoral dilemma of government parties. Coalition parties criticize each other abundantly but refrain from 'burning bridges' with their partners through virulent attacks. These findings have implications for studying negative campaigning and coalition politics.
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