Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Confere 2015
DOI: 10.3115/v1/p15-2072
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The Media Frames Corpus: Annotations of Frames Across Issues

Abstract: We describe the first version of the Media Frames Corpus: several thousand news articles on three policy issues, annotated in terms of media framing. We motivate framing as a phenomenon of study for computational linguistics and describe our annotation process.

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Cited by 160 publications
(190 citation statements)
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“…Based on this guidance and the difficulty of labeling tweets (as discussed in Card et al (2015)), annotators were instructed to use the following procedure: (1) attempt to assign a primary frame to the tweet if possible, (2) if not possible, assign two frames to the tweet where the first frame is chosen as the more accurate of the two frames, (3) when assigning frames 12 through 17, double check that the tweet cannot be assigned to any other frames. Annotators spent one month labeling the randomly chosen tweets.…”
Section: Data Collection and Annotationmentioning
confidence: 99%
See 1 more Smart Citation
“…Based on this guidance and the difficulty of labeling tweets (as discussed in Card et al (2015)), annotators were instructed to use the following procedure: (1) attempt to assign a primary frame to the tweet if possible, (2) if not possible, assign two frames to the tweet where the first frame is chosen as the more accurate of the two frames, (3) when assigning frames 12 through 17, double check that the tweet cannot be assigned to any other frames. Annotators spent one month labeling the randomly chosen tweets.…”
Section: Data Collection and Annotationmentioning
confidence: 99%
“…While the first frame supports increasing minimum wage because it improves workers' lives, the second frame, by conversely emphasizing the costs involved, opposes the increase. Using framing to analyze political discourse has gathered significant interest over the last few years (Tsur et al, 2015;Card et al, 2015;Baumer et al, 2015) as a way to automatically analyze political discourse in congressional speeches and political news articles. Different from previous works which focus on these longer texts or single issues, our dataset includes tweets authored by all members of the U.S. Congress from both parties, dealing with several policy issues (e.g., immigration, ACA, etc.).…”
Section: Introductionmentioning
confidence: 99%
“…Despite these perspectives on framing and frames, some interpret frames instead as topics such as crime and punishment or health and safety (Card et al 2015). However, these topics are just the categories of the subject matter in news articles.…”
Section: Introductionmentioning
confidence: 99%
“…However, these topics are just the categories of the subject matter in news articles. Obviously, multiple frames can fall under any of the categories, explaining why some theorists such as Card et al (2015) claim that framing should be perceived as non-issue-specific and able to be analyzed with a fixed set of framing dimensions (topics), rather than being associated with a specific issue (Entman 1993, Chong andDruckman 2007). Nonetheless, we also agree with de Vreese (2005) that frames can be classified as generic or issue-specific; for example, economic benefits can be used as a generic frame for various issues.…”
Section: Introductionmentioning
confidence: 99%
“…Framing contextualizes the discussion by emphasizing specific aspects of the issue, which creates an association between the issue and a specific frame of reference. Research on issue framing in political discourse is rooted in social science research (Entman, 1993;Chong and Druckman, 2007) and recently has attracted growing interest in the natural language processing community (Tsur et al, 2015;Card et al, 2015;Baumer et al, 2015) as a way to automatically analyze political discourse in congressional speeches and political news articles. Contrary to these sources, Twitter requires politicians to compress their ideas and reactions into 140 character long tweets.…”
Section: Introductionmentioning
confidence: 99%