Enabling the Social Web

Krishna P. GummadiNetworked Systems Group

Max Planck Institute for Software Systems

My research

• Understand and build complex networked systems

• Examples:– social web systems: e.g., Facebook, Twitter, YouTube

– Internet access networks: e.g., cable, DSL broadband

– peer-to-peer systems: e.g., BitTorrent, Skype

• Aspects of their complexity– massive scale– tremendous heterogeneity– decentralized control

My methodology

• First understand and then build – observe deployed systems– extract feedback– test new designs and architectural principles

• Why understand?– Can’t predict overall system behavior from first principles alone

• much like social, economic, or political systems

The big picture

Three fundamental trends & challenges in social Web

1. User-generated content sharing– can we protect privacy of users sharing personal data?

2. Word-of-mouth based content exchange– can we understand & leverage word-of-mouth better?

3. Crowd-sourcing content rating and ranking1. can we find trustworthy & relevant content sources?

Challenge:Privacy concerns with personal data sharing

• The traditional web (1993 – )– Publishers: Companies, Universities, & Governments– Content: Public information– Openness & universal access were key goals

• The social web (2005 – )– Publishers: Individuals– Content: Private photos & videos– Privacy and access control are key challenges

Research problems

• Data uploaded to social networking sites– Can we use home network infrastructures to share data?

[NOSSDAV ’11]

• Data given to 3rd party social networking apps– Can we use trusted cloud infrastructures to host apps? [WOSN

’12]

• Data shared with other users in the network [IMC ’11]

– Can we design better access control mechanisms?– Can we design abstractions to control data exposure?

• Data implicitly leaked by friends [WSDM ’10]

Challenge:Understanding dynamics of word-of-mouth

• Discovering information on the Web– Old method: Browsing from authoritative sources– New method: Word-of-mouth from friends

• Lots of theories & beliefs about viral propagation – But few are empirically derived or validated at scale!

• Large-scale empirical studies only possible recently– Measurements of social network graphs, their evolution, & user

activity [IMC ‘07, UbiComp ’07, WOSN ’08, WOSN ’09]

Research problems

• Understand dynamics of propagation– Temporal and spatial patterns of propagation– Role of social network, social systems, and user influence

• For different types of information and innovations– News, web URLs, conventions, and technology services

• With the ultimate goal of enabling better viral campaigns– Consumers: Help them get content they would not otherwise receive– Publishers: Help them spread their content more effectively

Studies of information diffusion

• How photos spread in Flickr [WOSN ’08, WWW ’09]

• How web URLs & news spread in Twitter [IMC ’11, ICWSM ‘11]

• The role of influencers and offline geography in information dissemination in Twitter [ICWSM ’10, ’12]

• Understanding and predicting the spread of social conventions in Twitter [ICWSM ’12, CIKM ’12]

Challenge:Finding relevant & trustworthy content

• Traditional web search leverages content-content links

• But, social web content is often multimedia & real-time– No links to other content

• Instead, the content is ranked collaboratively by users

• Concerns with relevancy and trustworthiness of users– How to identify users with similar interests – How to separate authoritative sources from spammers

Research problems

• Observation: Content is inter-linked via social network

• Leverage user network & activities – To find people with similar interests? – To isolate malicious users and

spammers?

• Concerns: anonymity & privacy

Social networks

Web

Finding trustworthy

& relevant

users

• Built systems that leverage social network to– limit the amount of spam in communications [NSDI ’08]

– limit large-scale data aggregation in social sites [CoNext ’12]

– improve web search results [HotNets ’06]

– detect or tolerate Sybil identities [SIGCOMM ’10, EuroSys ‘12]

• Finding authoritative & trustworthy users in Twitter– understanding & combating link farming [WWW ’12]

– identifying & ranking topical experts [WOSN ’12, SIGIR ’12]