YouTube: sharing AV content as a collective effort

Courtois, CédricOstyn, Valerie

Mechant, Peter

MICT-IBBT-Ghent Univeristy

•Launched in 2005

•Accounts for 20% of the HTTP traffic

•78 billion videos in 2008

•150.000 videos are added every day

•Professional content is the most popular

•Majority of content is user-generated

However, very low seeder-leecher ratio:90% of the video clips shared by 6% of its subscribers.

What motivates users to upload content?

Social loafing:

‘the reduction in motivation and effort when individuals work collectively compared with when they work individually or coactively’ (Karau & Williams, 1993, p. 681)

Historically a major topic in social psychology

•Experiment by Max Ringelmann (1913): one of the earliest discoveries in the field of social psychology

•Large body of research throughout the years

•Authorative meta-analysis by Karau and Williams (1993)

•Based on 178 units of analysis•Led to the formulation and validation of the Collective Effort Model (CEM)

From Kravitz & Matin (1986)

The Collective Effort Model

Adapted from Karau & Williams (1993)

•What motivates the individual to engage and perform within a group activity?

•Follows an expectancy-value ratio (cf. Rotter, Ajzen and Fishbein)

Does the CEM apply to YouTube?•The seeder as an individual•YouTube as a collective

Methodology•In depth, semi structured, face to face interviews with 20 seeders•Aimed at maximal variety in number and content of clips (2-209)•Computer assisted analysis: Nvivo 8

Finding 1: Double articulation of the collective effort model

•Clips often made in a group setting: role distribution

•The collective effort of production precedes the collective effort of uploading

Recorded and uploaded by Sarah (22), a gymnastics coach Recorded and uploaded by Patrick (25), a boy scout

Finding 2: Group perception in the collective effort of uploading is multidimensional

Group 1 of 3: Identified offline group•Physical acquaintance between uploader-group members•Possible overlap with production group

E.g. When a clip is uploaded to share it with the people directly involved: Patrick’s (25) fellow scouts

Finding 2: Group perception in the collective effort of uploading is multidimensional

Group 2 of 3: Identified online group•No physical acquaintance, though clear conception•Uploader and group members often share a common interest

E.g. Tyler (18) uploads recordings of him guitar, aiming at other musicians

Finding 2: Group perception in the collective effort of uploading is multidimensional

Group 3 of 3: Unidentified online group•No psyhical acquaintance, nor clear conception•YouTube community as a whole, viewers passing by as clips are public and searchable

E.g. Judy (20) posts vlogs sponsored by Google ads, benefiting directly from a large audience

Finding 3: Prefered feedback corresponds with groups concepts

Quantitative feedback: rates, views

Qualitative feedback: comments

Identified offline group: prefers qualitative

Identified online group: appreciates both qualitative/quantitative

Unidentified online group: mostly prefers quantitative

Conclusion:•CEM does not immediately fit

•It provides the necessary building blocks for a domain-specific model

•Differentiation between online collective effort, offline collective effort and group multidimensionality need to be incorporated

Limitations and implications:•Limited sample? Even in this small sample a differentiation in groups emerges, rendering the CEM unfit

•Only applicable to YouTube? Necessity to perform similar research on other online communities such as Flickr, Deviantart, Social Network Sites such as Facebook, Myspace, etc.

•Research in the pipeline: quantification of the conceptual model

Contact: Cedric.Courtois@Ugent.be

Thank you for listening…Any questions?

Kwalitatieve analyse

• Kwalitatief onderzoek: deductief versus inductief (grounded theory)

• Essentie: vanuit een bulk aan narratieve informatie concepten en verbanden filteren.(In dit geval 20*15=300 pagina’s aan transcripties)

Kwalitatieve analyse: praktisch

• Old skool: alles uitprinten en puzzelen…• Absoluut niet dynamisch• Vreselijk bij overvloed aan data

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Kwalitatieve analyse: praktisch

• Software: NVivo, Altlas.ti (NVivo vind je op athena.ugent.be)•Transcripties inladen: alles wordt verzameld (digitaal werkblad)

Kwalitatieve analyse: praktisch

• Software: NVivo, Altlas.ti•Annoteren in de tekst

Kwalitatieve analyse: praktisch

• Software: NVivo, Altlas.ti•Concepten filteren: free nodes, tree nodes (dynamisch aanpasbaar)

Kwalitatieve analyse: praktisch

• Software: NVivo, Altlas.ti•Stukken transcript worden gegroepeerd in de node •Makkelijk van free nodes naar tree nodes

Kwalitatieve analyse: praktisch

• Software: NVivo, Altlas.ti•Gebruik van querries: geautomatiseerde opdrachten (vb. woordfrequentie)

Kwalitatieve analyse: praktisch

• Voordelen zijn evident:•Cleane benadering van de data: geen gerommel met papieren•Beter overzicht: je haalt meer uit de data•Hulp door geautomatiseerde opdrachten

•Eén grote MAAR:•Software is een hulpmiddel: het denkwerk/interpretatie blijft afhangen van de onderzoeker. Kwalitatief onderzoek blijft in wezen een subjectieve bezigheid (kwantitatief onderzoek evenzeer…)

Zelf aan de slag?

• NVivo:•Pakket lijkt aanvankelijk zeer complex, maar wijst zichzelf uit•Handleidingen en tutorials zijn makkelijk te vinden. Enkele suggesties:

•“Teach yourself Nvivo 8”: http://qsrinternational.fileburst.com/Document/NVivo8/Teach_Yourself_NVivo_8_Tutorials.pdf•“Qualitative Data Analysis with Nvivo” (Bazely, 2007) @ PS04.SOOI026A (Sociologie)•Etc.