FrameAxis: Characterizing Microframe Bias and Intensity with Word Embedding
| Autor: | Jisun An, Haewoon Kwak, Elise Jing, Yong-Yeol Ahn |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
050402 sociology Word embedding General Computer Science Computer science Process (engineering) computer.software_genre Antonyms World Wide Web and Web Science Computer Science - Computers and Society Social Computing 0504 sociology Argument Framing (construction) Computers and Society (cs.CY) 050602 political science & public administration Set (psychology) Computer Science - Computation and Language SemAxis business.industry I.2.7 Data Science Semantic Axis 05 social sciences Microframe QA75.5-76.95 Media bias Natural Language and Speech 0506 political science Computational Linguistics Electronic computers. Computer science Scalability Domain knowledge Framing Artificial intelligence business computer Computation and Language (cs.CL) Natural language processing |
| Zdroj: | PeerJ Computer Science, Vol 7, p e644 (2021) PeerJ Computer Science |
| DOI: | 10.48550/arxiv.2002.08608 |
| Popis: | Framing is a process of emphasizing a certain aspect of an issue over the others, nudging readers or listeners towards different positions on the issue even without making a biased argument. {Here, we propose FrameAxis, a method for characterizing documents by identifying the most relevant semantic axes ("microframes") that are overrepresented in the text using word embedding. Our unsupervised approach can be readily applied to large datasets because it does not require manual annotations. It can also provide nuanced insights by considering a rich set of semantic axes. FrameAxis is designed to quantitatively tease out two important dimensions of how microframes are used in the text. \textit{Microframe bias} captures how biased the text is on a certain microframe, and \textit{microframe intensity} shows how actively a certain microframe is used. Together, they offer a detailed characterization of the text. We demonstrate that microframes with the highest bias and intensity well align with sentiment, topic, and partisan spectrum by applying FrameAxis to multiple datasets from restaurant reviews to political news.} The existing domain knowledge can be incorporated into FrameAxis {by using custom microframes and by using FrameAxis as an iterative exploratory analysis instrument.} Additionally, we propose methods for explaining the results of FrameAxis at the level of individual words and documents. Our method may accelerate scalable and sophisticated computational analyses of framing across disciplines. Comment: 24 pages; published in PeerJ CS |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|