1. Enabling the Social Web Krishna P. Gummadi Networked Systems Group Max Planck Institute for Software Systems

2. 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

3. 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

4. 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 ranking –can we find trustworthy & relevant content sources?

5. 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

6. Research problems Data uploaded to social networking sites –Can we use home network infrastructures to share data? [NOSSDAV ’11] Data given to 3 rd 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]

7. 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]

8. 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

9. 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]

10. 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

11. 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

12. 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]