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An Exploratory Study on User Intentions for Video Production.
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This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0
Why did you record this video?
An exploratory study on user intentions for video
production.
Mathias Lux* & Jochen Huber§
* Klagenfurt University, AT§ Technische Universität Darmstadt, DE
Motivation
• Novelty of intentions in MMIS
– have not (yet) been investigated thoroughly
• Hard, interdisciplinary problem
– fuzzy, social, deals with people
• Intentions are diverse
– have potential for distinguishing between different user groups
(cc) by bitzcelt, http://www.flickr.com/photos/bitzcelt/
Goals
• Find out if there is a taxonomy that can
be used for MMIS.
• Support or reject current approaches.
• Find path towards a usable model
supported by statistics.
(cc) by jam343 http://www.flickr.com/photos/jam343
Roots
• A taxonomy of web search (2002), A. Broder
– navigational – “dublin wikipedia”
– informational – “day tour dublin”
– transactional – “book hotel in dublin”
• Understanding user goals in web search
(2004), D. Levinson & D. Rose
– transactional -> resource
– more fine-grained sub categories
– informational > 60%
Roots
• A classification scheme for user
intentions in image search (2010), M.
Lux, C. Kofler, O. Marques
– 4th category: mental image
– categories overlap
Photo production
• The ubiquitous camera: An in-depth
study of camera phone use (2005), T.
Kindberg et al.
– Affection vs. function
– Social vs. individual
Video production
• Video microblogging: your 12 seconds of
fame (2010) N. Bornoe & L. Barkhuus
– social collaboration (not individual)
– self expression, entertainment, self representation
• Practices in creating videos with mobile
phones (2009), A. Puikkonen et al.
– preserve moment of interest
– sharing ”occasionally”, not by default
Methodology
• Exploratory study
– 20 participants (16m, 4f)
– semi-structured interviews
• Interviews
– demographics & general usage
– communication & recording habits
• Instances
– 48 situations were reported
Research Questions
Using Kindberg’s taxonomy as a basis
• Are Kindberg’s classes disjoint?
– are there instances that indicate overlap?
• Is a 2D space sufficient to describe video
production intentions
– need for other dimensions?
(cc) by oberazzi, http://www.flickr.com/photos/oberazzi/
Analysis
• Clustering of instances
– similar instances go together
– grouped manually
– discussed grouping
– multiple assignments possible
(cc) by alastanton, http://www.flickr.com/photos/alanstanton
Clustering
• Preservation
– Storing a scene to view it later
• Sharing
– Showing scenes to others
• Affection
– Capturing a scene due to emotion
• Functional
– Video is part of a job, hobby, etc.
• Technical interest
– E.g. trying out a camera
• Other
– Unknown or unmentioned intentions, etc.
Results
• Nearly all of the videos (39 /48) were
taken for sharing them.
– 29 of the 39 instances: family, friends, colleagues, other closed groups.
• Affection - 23 instances
• Preservation - 19 instances
Results
• Do class assignments co-occur?
• Cross-tabulation
– phi can be read like a correlation coefficient
– -1 <= min <= phi <= max <= 1
– min, max due to different number ofassignments
Discussion
• Multiple assignments
– 81% were assigned to more than 1 cluster
– Are classes disjoint (e.g. function vs. affection)?
P4 mentioned a video he took on a mountain while
snowboarding. He recorded the video because he “took
it because [he loves] snowboard video tricks and [he
thinks] that it is very important to reconsider them to
improve [his own] technique”.
Discussion
P10 reported “First my friend is so good at
singing and also charming and second he was
about to leave the city and that was our last
meeting. So I took the video to remember the
night”
• Ad-hoc affection vs.
• Preservation
Discussion
• Preservation opposite of sharing?
– No correlation in our data (A)
• Function & preservation go together?
– maximum neg. correlation (B)B
A
Conclusion
• Intention classes not disjoint in the
domain of video production
• Kindberg’s taxonomy is not sufficient for
video production
• Preservation, sharing, affection &
function are 4 valid classes to start with.
Future work
• Our proposed structure is biased by
– the small data set
– the convenience sample
– the questions asked
• Collected a data set for photos
– 1,309 photos + intentions of their photographers
– mturk validation and QA of the survey results
• Collection of a video data set
• Application in domains
(cc) by thevince, http://www.flickr.com/photos/thevince
Thanks …
.. for your interest
more on user intentions:
http://tinyurl.com/mlux-itec
check out LIRe CBIR library:
http://www.semanticmetadata.net
(cc) http://www.jumpingbrain.org/