- visuals as content (and some reception of visuals later on)
- content analysis is under-utilized in communication research ?
- human coders are the gold standard, but does not scale up
- digital images, videos, web pages, source code, metadata
- dont forget subtitles, automatic transcription of video
- cf. "manifest content" in web pages, videos
- videos need to be split into frames, images
- pixels, color histogram, dimensions, edges
- cf. "latent content", infer content features
- low, mid, high level features, lower are easier
- image saliency is mid-level feature? more computationally intensive..
- understand, infer relationships between images
- image embeddings, vector space, topic modelling for images
- high-level content features, object categories inferred from pixels
- does image contain faces, people or not, binary classification
- try an image classifier
- some instructions here
- open question: when could binary image classification be useful in communication research ?
- e.g. distinguish content from advertising..
- detect multiple basic object classes, aka labelling
- face identification is a special case of this approach
- typically also involves object localization
- get bounding boxes of multiple objects
- try it at google vision ai
- try it at ms computer vision
- open question: when could object detection be useful in communication research ?
- outline actual pixels covered by each object in image
- web page segementation, (quite difficult!)
- open question: when could object detection be useful in communication research ?
- e.g. large scale video analysis..
- infer body parts and relationships
- if applied to face images, becomes image sentiment analysis
- extract text from images, screenshots, etc
- could be used as input to word vectors
- open question: when could image captioning be useful in communication research ?
- actions, actors, shot detection, data reduction
- not in the visual domain, so not dealt with here
- reception of visuals
- media psychology
- advert content features and product attitude
- product visual saliency and visual attention (eye-tracking)
- production of visuals
- facilitate understanding, comprehension of big data
- gans, artwork, people, dall-e 2 (not content analysis)
- avatars, virtual models