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Media Framing
Media Framing
Media framing refers to the process through which media outlets present and shape information, events, or issues in a particular way to influence the audience’s perception and understanding. Framing involves selecting certain aspects of a subject and emphasizing them, while downplaying or ignoring others, to construct a specific narrative or perspective. This process can be both intentional and unintentional, and it plays a crucial role in shaping public opinion and guiding public discourse.
Media Framing can be achieved through
Various techniques, such as:
Selection:
Choosing which stories to cover and which to ignore, based on factors like news value, audience interest, or editorial policy.
Emphasis:
Highlighting specific aspects of a story, such as particular facts, opinions, or images, to create a specific impression or narrative.
Language and rhetoric:
Using particular words, phrases, or rhetorical devices to convey a certain tone or viewpoint, such as using emotionally charged language or employing metaphors and analogies.
Headlines and visuals:
Crafting headlines, captions, images, or graphics that support a particular frame or perspective, and draw the audience’s attention to specific aspects of the story.
Sourcing:
Selecting and presenting quotes, sources, or experts that support the desired frame or narrative, while downplaying or ignoring alternative viewpoints or contradictory evidence.
Media framing can have a significant impact on the way audiences perceive and understand events or issues. Different frames can lead to different interpretations, evoke different emotions, and shape public opinion in various ways. It is essential for consumers of media to be aware of framing and develop critical thinking skills to assess the information presented to them, considering alternative perspectives and sources to form a well-rounded understanding of the subject matter.
What is Media Framing Detection?
Media framing detection is the process of identifying and analyzing how media outlets present and shape information, events, or issues to influence the audience’s perception and understanding. Detecting media framing can help reveal the underlying narratives, biases, or perspectives that may be guiding a particular news story, article, or broadcast.
The manual approach to media framing requires examining aspects such as language, tone, sourcing, headlines, visuals, and the overall narrative to identify the frame being used. It involves recognizing patterns, comparing and contrasting different media sources, and assessing the consistency and credibility of the information presented.
Automatic detection of media frames involves using computational techniques, such as natural language processing, machine learning, and text analysis, to automatically identify and classify media frames. Automatic detection can involve analyzing large volumes of text or other media content to identify patterns, trends, and features associated with specific frames.
Media framing detection is crucial for maintaining the integrity of information and promoting informed decision-making. By identifying and understanding media framing, audiences can become more aware of potential biases, recognize alternative perspectives, and develop a more balanced and nuanced understanding of the issues being covered.
There has been research for the automatic detection of media framing. Boydstun et al. [Ref] developed Policy Frames Codebook containing 14 framing dimensions and (plus an “other” category), resulting 15 frame categories that are intended to be applicable to any policy issue (abortion, immigration, foreign aid, etc.) and in any communication context (news stories, Twitter, party manifestos, legislative debates, etc.). Following that Card et al. developed a corpus, known as media frame corpus, which has been used in much research to develop AI models.