“I Like” - Analysing Interactions within Social Networks to Assert the Trustworthiness of Users, Sources and Content

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The 5th IEEE International Conference on Social Computing (SocialCom 2013) / Washington, DC, USA / 10th September 2013

The 5th IEEE International Conference on Social Computing (SocialCom 2013) / Washington, DC, USA / 10th September 2013

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  • Generic privacy settings – SN do not provide privacy settings for each part of the information SNs do not cater for trust, neither capture trust not even provide users to enter any trust information Assumes that every person in ones social graph has the same trust level Does not provide users to enter trust values for different user lists Cumbersome to manage contacts with respect to trust
  • We aim to answer these questions by analysing user interactions in Social Networks
  • The results show that participants prefer to share content from external sources into Twitter and Facebook since 39% of the participants frequently share within Twitter and 33% within Facebook. 31% of the participants occasionally share external content into Twitter and 47% into Facebook. LinkedIn is the least used for sharing external content since 64% never share external content into LinkedIn. Google+ is also not popular for sharing since 40% never share content within Google+.
  • The results show that participants prefer to re-share or retweet from within Twitter since 38% of the participants frequently retweet within Twitter. Although only 16% frequently re-share in Facebook, 56% do re-share occasionally whilst in Twitter 34% occasionally retweet. LinkedIn is the least popular Social Network for re-sharing since 70% never re-share. Google+ is the second least preferred Social Network since 46% never re-share.
  • The results show that the “Like” button in Facebook is the most frequently used since 48% of the participants use that functionality whereas only 13% frequently use the “ Favourite ” button in Twitter. However, 39% occasionally use the “ Favourite ” button in Twitter whereas 36% occasion- ally use the “ Like ” button in Facebook and in Google+ 36% occasionally use the “ +1 ” . LinkedIn is the least preferred Social Network for using the “ Like ” button since 66% never use this functionality. In Google+, 35% of the participants never use the “ +1 ” and in Twitter, 30% of the participants never use the “ Favourite ” button.
  • In Facebook, 69% “Like” comments, 67% “Like” status updates, 63% “Like” photos, 54% “Like” external content, 34% like videos and 32% like profile updates. In the other Social Networks, the results show a similar trend whereby participants “Like”, “+1” or “ Favourite ” more status updates, comments, external content and photos. In Twitter for in- stance, external content is the most “ Favourite ” since 35% of the participants “ Favourite ” external content and 31% “ Favourite ” status updates. Whereas in Google+, 25% of the participants “ +1 ” external content and 20% “ +1 ” status updates. Once again, LinkedIn is the least preferred Social platform for using the “ Like ” button.
  • The results show that commenting in Facebook and replying in Twitter are the most frequently used since 31% frequently comment in Facebook and 25% reply in Twitter. Moreover, 52% occasionally comment in Facebook and 38% reply in Twitter. Once again, LinkedIn is the least preferred Social Network for commenting since 67% partic- ipants never comment in LinkedIn. Moreover, in Google+ 46% never comment.
  • The results show that tagging and mentioning other users is the least of the user interaction types used. Only 24% frequently tag in Facebook; 21% frequently mention other users in Twitter and in Google+ only 1% frequently tag. Moreover, 42% occasionally tag users in Facebook, 42% occasionally mention users in Twitter and 19% occasionally tag users in Google+. Again, LinkedIn is the least preferred Social Network for tagging since 78% of the participants never tag. In twitter, 21% never mention users; in Facebook 24% never tag users; and in Google+ 54% never tag.
  • From the results it can be noted that 65% of the participants are more concerned with trusting the source. 57% of the participants perceive trust as trust in the content and trust in the belief that a person will act according to the user’s expectations. Surprisingly, only 45% of the participants have selected that trust means a person is reliable if s/he shares content with the participant.
  • The results show that participants trust the content most when they use the share button.
  • Participants also perceive trust- ing the content more whilst re-sharing or retweeting what other users have already shared. This result and the result of sharing external content within these Social Networks illustrate that the act of sharing results more in trusting the content.
  • The results once again show that by using these features, participants trust more the content. Moreover, the results also show that by using this functionality, participants trust more the person who is sharing the content rather than the source who created the content. Therefore, the “Like”, “+1” and “ Favourite ” buttons are used to capture the trust for the content and the trust for the person sharing the content.
  • With this user interaction type, participants trust more the person who created the post. This is because comments or replies might also contain content that might reflect distrust in the content or source. This is illustrated in the results as 37% of the participants trust the content and 24% trust the source whilst commenting or replying. In this study we only focus on how we can capture trust from the act of the interactions. However, it would be interesting as future work to analyse how to capture trust or distrust from the semantics of the comments or replies.
  • The results show that the participants trust more the person who they are tagging.
  • These results illustrate that the perception of trust in the other person tagging or mention the user is lower than the perception of trust in the other person being tagged or mentioned by the user.
  • These results show that all the user interaction types are mostly used in Facebook and Twitter. Hence, these Social Networks are the optimal for capturing trust for these social user interaction types.
  • These results illustrate that we can therefore correlate trust and the user interactions as follows: The trust for the source who created the content can be captured using (1) the sharing and (2) the re-sharing or retweeting user interactions; The trust for the content can be captured using (1) the sharing, (2) the re-sharing or retweeting; and (3) the “like” or “+1” or “ favourite ” user interactions; and The trust for the user requesting personal information (i.e. the person) can be captured using (1) the “like” or “+1” or favourite; (2) the comments or replies; (3) tags or mentions and (4) tagged or mentioned user interactions.
  • The user’s subjective trust value for the source can therefore be calculated as the weighted average of all the shares and re-shares of content related to the source weighted by the trust of users either directly connected to the user or indirectly connected through other users that have shared and re-shared any content related to the source.
  • The user’s subjective trust value for the content can therefore be calculated as the weighted average of all the shares; re-shares or retweets; likes, +1s and favourites of the content; weighted by the trust of users either directly connected to the user or indirectly connected through other users that have shared, re-shared, liked, +1 and favourite the same content.
  • The user’s subjective trust value for the requester can therefore be calculated as the relationship between the sum of interactions consisting of: the “likes”, “+1s” or “ favourites ” of the content related to the user and the requester; the comments or replies between the user and the requester; the tags of or tagged by the user and the requester; and the total sum of all the interactions of the user.

Transcript

  • 1. © Copyright 2013 INSIGHT Centre for Data Analytics. All rights reserved. INSIGHT Centre for Data Analytics www.insight-centre.org Semantic Web & Linked Data Research Programme “I Like” – Analysing Interactions within Social Networks to Assert the Trustworthiness of Users, Sources and Content Owen Sacco & John G. Breslin owen.sacco@deri.org & john.breslin@nuigalway.ie ASE/IEEE SocialCom 2013 Washington, DC, USA Tuesday 10th September 2013
  • 2. INSIGHT Centre for Data Analytics www.insight-centre.org Semantic Web & Linked Data Research Programme Introduction  Current Social Networks:  Provide generic privacy settings for sharing information  Do not take user’s trust into account
  • 3. INSIGHT Centre for Data Analytics www.insight-centre.org Semantic Web & Linked Data Research Programme Introduction In reality, we only share parts of our information with whomever we trust
  • 4. INSIGHT Centre for Data Analytics www.insight-centre.org Semantic Web & Linked Data Research Programme Social Factors  Trust judgments are influenced by Social Factors:  Past interactions with a person  Opinions of a person’s actions  Other people’s opinions  Rumours  Psychological factors impacted over time  Life events  and so forth  These can be hard to compute since the information required is limited and unavailable in Social Networks
  • 5. INSIGHT Centre for Data Analytics www.insight-centre.org Semantic Web & Linked Data Research Programme Research Questions 1. What is the user’s perception of trust? 2. Which information extracted from Social Networks is useful for computing trust? 3. For what and for whom can trust be computed from the information in Social Networks?
  • 6. INSIGHT Centre for Data Analytics www.insight-centre.org Semantic Web & Linked Data Research Programme User Survey  To analyse how trust can be inferred from Social Networks  We focus on whether trust can be asserted from user interactions within the Social Networks  User interactions with:  Other users  Content shared within the Social Networks
  • 7. INSIGHT Centre for Data Analytics www.insight-centre.org Semantic Web & Linked Data Research Programme User Survey  User interactions:  Sharing of content from external sources  Re-sharing or retweeting content  “Like”, or “+1” or “favourite”  Comments or replies  Tags or mentions
  • 8. INSIGHT Centre for Data Analytics www.insight-centre.org Semantic Web & Linked Data Research Programme User Survey  178 participated in the survey: 65% male and 35% female  Age:  Social Network Accounts: Age Category Participants 18 - 20 3% 21 - 29 45% 30 - 39 32% 40 - 49 13% 50 - 59 6% 60+ 1% Social Networks Participants Facebook 88% Google+ 69% Twitter 82% LinkedIn 85% None of the Above 1%
  • 9. INSIGHT Centre for Data Analytics www.insight-centre.org Semantic Web & Linked Data Research Programme User Survey  Occupations of participants Occupation Categories Participants Computer and Mathematics 59% Education, Training and Library 26% Business and Financial 13% Management 8% Architecture and Engineering 6% Arts, Design, Entertainment, Sports and Media 5% Life, Physical and Social Sciences 3% Office and Administrative Support 2% Healthcare Support 1% Community & Social Service 1% Sales 1% Unemployed 1%
  • 10. INSIGHT Centre for Data Analytics www.insight-centre.org Semantic Web & Linked Data Research Programme User Survey  Two parts: 1. Usage Patterns 2. User’s Trust Perception in Social Networks  Usage Patterns: how often users use each social user interaction and on which Social Network  User’s Trust Perception: analyses what users trust when they use these social user interactions
  • 11. INSIGHT Centre for Data Analytics www.insight-centre.org Semantic Web & Linked Data Research Programme  How often do you share content from external sources within Facebook, Google+, Twitter and LinkedIn? User Survey: Usage Patterns
  • 12. INSIGHT Centre for Data Analytics www.insight-centre.org Semantic Web & Linked Data Research Programme User Survey: Usage Patterns  How often do you re-share or retweet what other users share within Facebook, Google+, Twitter and LinkedIn?
  • 13. INSIGHT Centre for Data Analytics www.insight-centre.org Semantic Web & Linked Data Research Programme User Survey: Usage Patterns  How often do you use the like, +1 and favourite buttons on Facebook, Google+, Twitter and LinkedIn?
  • 14. INSIGHT Centre for Data Analytics www.insight-centre.org Semantic Web & Linked Data Research Programme User Survey: Usage Patterns  For what do you use the like, +1 and favourite buttons on Facebook, Google+, Twitter and LinkedIn?
  • 15. INSIGHT Centre for Data Analytics www.insight-centre.org Semantic Web & Linked Data Research Programme User Survey: Usage Patterns  How often do you comment or reply on Facebook, Google+, Twitter and LinkedIn?
  • 16. INSIGHT Centre for Data Analytics www.insight-centre.org Semantic Web & Linked Data Research Programme User Survey: Usage Patterns  How often do you tag or mention other users on Facebook, Google+, Twitter and LinkedIn?
  • 17. INSIGHT Centre for Data Analytics www.insight-centre.org Semantic Web & Linked Data Research Programme User Survey: User’s Trust Perception  What is your perception of the meaning of the word “Trust”?
  • 18. INSIGHT Centre for Data Analytics www.insight-centre.org Semantic Web & Linked Data Research Programme User Survey: User’s Trust Perception  What do you trust when you share external content into Facebook, Google+, Twitter and LinkedIn?
  • 19. INSIGHT Centre for Data Analytics www.insight-centre.org Semantic Web & Linked Data Research Programme User Survey: User’s Trust Perception  What do you trust when you re-share or retweet content within Facebook, Google+, Twitter and LinkedIn?
  • 20. INSIGHT Centre for Data Analytics www.insight-centre.org Semantic Web & Linked Data Research Programme User Survey: User’s Trust Perception  What do you trust when you like, +1, or favourite within Facebook, Google+, Twitter and LinkedIn?
  • 21. INSIGHT Centre for Data Analytics www.insight-centre.org Semantic Web & Linked Data Research Programme User Survey: User’s Trust Perception  What do you trust when you comment or reply to posts within Facebook, Google+, Twitter and LinkedIn?
  • 22. INSIGHT Centre for Data Analytics www.insight-centre.org Semantic Web & Linked Data Research Programme User Survey: User’s Trust Perception  What do you trust when you tag or mention other users within Facebook, Google+, Twitter and LinkedIn?
  • 23. INSIGHT Centre for Data Analytics www.insight-centre.org Semantic Web & Linked Data Research Programme User Survey: User’s Trust Perception  What do you trust when you are tagged or mentioned within Facebook, Google+, Twitter and LinkedIn?
  • 24. INSIGHT Centre for Data Analytics www.insight-centre.org Semantic Web & Linked Data Research Programme User Survey: Summary  Overall Participants’ Activity of Social User Interactions
  • 25. INSIGHT Centre for Data Analytics www.insight-centre.org Semantic Web & Linked Data Research Programme User Survey: Summary  Overall Participants’ Perception of Trust
  • 26. INSIGHT Centre for Data Analytics www.insight-centre.org Semantic Web & Linked Data Research Programme Asserting Trust: Trusting the Source  Trust for the source can be asserted from:  The share button  The re-share or retweet buttons  Trusting the source: » denotes the user’s subjective trust value for a particularτ source » w denotes the trust a third party user has in the user’s social graph » s denotes the number of shares and re-shares related to the source
  • 27. INSIGHT Centre for Data Analytics www.insight-centre.org Semantic Web & Linked Data Research Programme Asserting Trust: Trusting the Content  Trust for content can be asserted from:  The share button  The re-share or retweet buttons  The like, +1 and favourite buttons  Trusting the content: » denotes the user’s subjective trust value for a particularτ content » w denotes the trust value a third party user has in the user’s social graph » c denotes the number of shares, re-shares, likes, +1s and favourites related to the content
  • 28. INSIGHT Centre for Data Analytics www.insight-centre.org Semantic Web & Linked Data Research Programme Asserting Trust: Trusting the User  Trust for the user (i.e. requester) can be asserted from:  The like, +1 and favourite buttons  The comments or replies to posts  The tags of the requester tagged by the information owner  The tags of the information owner tagged by the requester  Trusting the user (i.e. requester): » denotes the user’s subjective trust value for a particularτ requester » r denotes the number of likes, +1s, favourites, comments, replies and tags related to the user and the requester
  • 29. INSIGHT Centre for Data Analytics www.insight-centre.org Semantic Web & Linked Data Research Programme Conclusion & Future Work  We focused on:  Analysing the user’s perception of trust and  How trust can be inferred from Social Networks  User survey that analysed the usage patterns and the user’s trust perception of:  The share button  The re-share or retweet buttons  The like, +1 or favourite buttons  The comment or reply buttons  The tag or mention buttons/options
  • 30. INSIGHT Centre for Data Analytics www.insight-centre.org Semantic Web & Linked Data Research Programme Conclusion & Future Work  The results have revealed that users are concerned with asserting trust for:  The source that created the content  The content  The user requesting personal information  Future work:  Implementing the trust assertions in our Privacy Preference Framework (see previous publications) – To enforce privacy preferences based on these trust assertions
  • 31. INSIGHT Centre for Data Analytics www.insight-centre.org Semantic Web & Linked Data Research Programme Thanks! @owensacco owen.sacco@deri.org @johnbreslin john.breslin@nuigalway.ie