Characterizing Video Responses in Social Networks

Benevenuto, Fabricio, et al. “Characterizing Video Responses in Social Networks.” – 0804.4865.

Benevenuto, et al., characterized over 3.4 million video and 400,000 video responses collected from YouTube over a 7-day period. Among other reasons they found their characterization interesting, they cite a sociological reason, “relating to social networking issues that influence the behavior of users interacting primarily with stream objects, instead of textual content traditionally available on the Web.” (pg. 1?)

This article is relevant to my research, since it is a study of online video and video responses. However, it was conducted of YouTube videos and therefore not relevant to use of video in the classroom. That said, there are a number of points I found quite valuable. For example, they acknowledge that the video communication format is used as an alternative to textual communication, yet in a threaded, sequential manner that is more common of online textual communication.

From the introduction:

Online social video networking sites allow for video-based communication among their users. Video-based functions are offered as alternative to text-based ones, such as video reviews for products, video ads and video responses [13]. A video response feature allows users to interact and converse through video, by creating a video sequence that begins with an opening video followed by video responses from other users.

Among other results, their research revealed that,

“[T]he characteristics of social video sharing services are significantly different from those of traditional stored object workloads, based on text and image. Part of the difference stems from the change from textual communication to stream-based communication, creating a new paradigm for online communication.

This new paradigm, this use of video to communicate asynchronously in a way similar to–yet other than–textual methods, and in a way that allows some textual elements, is what I’m examining and hope to define and explain further.

I also take away from this article a few more ideas about how I can code the videos that are part of my data collection. Some of the questions that I might use in coding the videos include:

  • How long is the video?
  • How many text responses did it receive?
  • How many video responses did it receive?
  • What is the length of the responses (average)?
  • Who responded? (This will be a lot of work, but might show that ceratin communities were formed with X number of students tending to comment for often on each other’s videos than they commented on the other students.
    • If there are small communities, what is the common number of community participants?
  • Is it just a first-level response or are there more replies to the replies?
  • Does the original author post a response?
    • What % of responded videos received at least one self-response (response posted by the original author)?

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