understanding co-evolution of social and content networks on twitter

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TU Graz - Knowledge Management Institute 1 Philipp Singer 17.04.2012 Understanding co-evolution of social and content networks on Twitter Philipp Singer , Claudia Wagner, Markus Strohmaier Knowledge Management Institute and Know Center Graz University of Technology, Austria

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Workshop presentation at #msm2012 and #www2012 conference about "Understanding co-evolution of social and content networks on Twitter".

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Page 1: Understanding co-evolution of social and content networks on Twitter

TU Graz - Knowledge Management Institute

1

Philipp Singer 17.04.2012

Understanding co-evolution of social and content networks on Twitter

Philipp Singer, Claudia Wagner, Markus Strohmaier

Knowledge Management Institute and Know Center

Graz University of Technology, Austria

Page 2: Understanding co-evolution of social and content networks on Twitter

TU Graz - Knowledge Management Institute

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Philipp Singer 17.04.2012

Motivation

Social media applications allow users to share content and sozialize Many social links and a lot of content

Value of social media application depends on how it is used – i.e., activity of users

Which factors impact users‘ content-related activities (e.g., hashtagging or link usage) and users‘ social activities (i.e., following)?

Page 3: Understanding co-evolution of social and content networks on Twitter

TU Graz - Knowledge Management Institute

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Philipp Singer 17.04.2012

Aim

Explore bi-directional longitudinal influence patterns between social and content properties

Sample research questions Does growth of a user's followers increase the number of authored

tweets? Does an increase of used URLs of users also increases their usage

of hashtags? …

??

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Philipp Singer 17.04.2012

? ?

?

?

???

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Philipp Singer 17.04.2012

Experimental Setup

• Twitter Dataset 1,500 random users chosen from public timeline 30 days time period (15.03.2011 – 14.04.2011) Measure users’ social and content-related activities daily

Use social and content properties to describe and abstract those activities

Approach Monitor and analyze how users‘ social activities and content-related

activities (and related outcomes) co-evole over time (Wang and Groth 2010)

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TU Graz - Knowledge Management Institute

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Philipp Singer 17.04.2012

Methodology Multilevel autoregressive modeling Predict future outputs based on past outputs

Variables are measured at different levels Coefficients can vary from user to user Overall coefficients determine influence between

properties over time

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Philipp Singer 17.04.2012

Does growth of a user's followers increase the number of authored tweets?

?

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TU Graz - Knowledge Management Institute

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Philipp Singer 17.04.2012

?

Does an increase of used URLs of users also increases their usage of hashtags?

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TU Graz - Knowledge Management Institute

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Philipp Singer 17.04.2012

? ?

?

?

???

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Philipp Singer 17.04.2012

Conclusions

Driven by social factors

Attention of other users motivates individuals

Social media hosts can adopt and use these techniques

Page 11: Understanding co-evolution of social and content networks on Twitter

TU Graz - Knowledge Management Institute

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Philipp Singer 17.04.2012

Limitations & Next Steps

One small random Twitter dataset Apply techniques to further, larger datasets

Limited to some chosen properties Produce different networks

Hashtag co-occurrence Similarity networks

Try different model approaches Analyze interface changes (e.g., recommender)

Page 12: Understanding co-evolution of social and content networks on Twitter

TU Graz - Knowledge Management Institute

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Philipp Singer 17.04.2012

Thanks for your attention!

@ph_singer

@clauwa

@mstrohm

[email protected]

Social properties influence content properties

but not vice versa!