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![Initial Motivation
Partially inspired by Gladwell’s book, The Tipping Point [1], in which he discusses
how life can be thought of as an epidemic. Some criticism exists as to Gladwell’s
rigor, however for our use it is about inspiration and motivation not accuracy.
The Books Key Points “for our purposes”
• Actors (Connectors, Mavens, Salesmen).
• Information spreads like disease.
• Ideas reach a tipping point (critical mass).
Let’s Face It - Social Networks Are Fun
• We are a social species, that enjoy communicating and self adulation.
| Clarkson University 3/28](https://image.slidesharecdn.com/6c7a31f0-7827-48ad-bc3c-45b325fdf4c7-141221131633-conversion-gate02/75/ase-social-informatics-6-3-2048.jpg)
![Actor Descriptions
• Isolate (Developmental Psychology) [27]
• Connector (Tipping Point) [1]
– Star (Small World Problem) [26]
– Bridge (The Hidden Organizational Chart) [2]
– Liason (The Hidden Organizational Chart) [2]
• Maven (Tipping Point) [1]
• Salesmen (Tipping Point) [1]
| Clarkson University 6/28](https://image.slidesharecdn.com/6c7a31f0-7827-48ad-bc3c-45b325fdf4c7-141221131633-conversion-gate02/75/ase-social-informatics-6-6-2048.jpg)
![References
[1] Gladwell, M. (2000). “The tipping point”. Boston: Little, Brown and Company.
[2] Allen, H. T. (1976). “Communication networks - The hidden organizational chart”. The
Personnel Administrator, 21(6), 31-35.
[3] Arun Phadke, James Thorp. (1978). “Contracts and Influence”. Social Netowrks, 1:1-48
[4] Davis, A., et. al. (1941). “Deep South: A social Anthropological Study of Caste and
Class”. University of Chicago Press. Chicago, Ill.
[5] Freeman, L. (2004) “The Development of Social Network Analysis: A Study in the
Sociology of Science”. BookSurge, LLC. North Charleston, SC.
[6] Stanley Wasserman, Katherine Faust. (1994). “Social Network Analysis: Methods and
Applications”, Structural Analysis in the Social Sciences, 25 November 1994
[7] Donald Triner. (2010). “Publicaly Available Social Media Monitoring and Situational
Awareness Initiative,” Office of Operations Coordination and Planning: Departmetn of
Homeland Security, June 22 2010.
[8] Juris, Jeffrey. (2012). “reflections on #Occupy Everywhere: Social media, public space,
and emerging logics of aggregation”. American Ethnologist. Vol 39, No. 2, pp. 259-279.
[9] Sheedy, Caroline. (2011). “Social Media for Social Change: A Case Study of Social
Media Use in the 2011 Egyptian Revolution”. Capstone Project.
[10] Stark, Rodney. (1987). “Deviant Places: A Theory of the Ecology of Crime”. Criminol-
ogy, 25: 893âĂŞ910.
[11] Brett Stone-Gross, et al. (2011). “The underground economy of spam: a botmaster’s
perspective of coordinating large-scale spam campaigns.” In Proceedings of the 4th
USENIX conference on Large-scale exploits and emergent threats (LEET’11). USENIX
Association, Berkeley, CA, USA, 4-4.
[12] Taylor Dewey, et al. (2012). “The Impact of Social Media on Social Unrest in the Arab
Spring”. Stanford University - Defense Intelligence Agency Final Report.
[13] Christian Sturm and Hossam Amer. (2013). “The effects of (social) media on rev-
olutions: perspectives from egypt and the arab spring”. In Proceedings of the 15th
international conference on Human-Computer Interaction: users and contexts of use -
Volume Part III (HCI’13), Masaaki Kurosu (Ed.), Vol. Part III. Springer-Verlag, Berlin,
Heidelberg, 352-358.
[14] Woods, Richard. (2010). “Privacy is Dead?: Facebook’s Mark Zuckerberg says privacy
is dead. So why does he want to keeps this picture hidden?”. Times Newspapers Ltd.
[15] Statistics Brain. (2013). “’Facebook Statistics”. Statistic Brain Research Institute,
publishing as Statistic Brain. 6/23/2013. http://www.statisticbrain.com/facebook-
statistics/
[16] CBS News. (2012). “Twitter’s censorship plan rouses global furor”. Associated Press.
January 27, 2012
[17] Statistics Brain. (2013). “Twitter Statistics”. Statistic Brain Research Institute, pub-
lishing as Statistic Brain. 5/7/2013. http://www.statisticbrain.com/twitter-statistics/
[18] Bagley, Nick. (2012). “The Decline of Myspace: Future of Social Media”. Dream-
grow Digital. 8/13/2012. http://www.dreamgrow.com/the-decline-of-myspace-future-
of-social-media/
[19] alton, Antony, “Temporal Logic”, The Stanford Encyclopedia of Philosophy (Fall 2008
Edition), Edward N. Zalta (ed.)
[20] Shea Bennett. “Just How Big Is twitter In 2012 [INFOGRAPHIC]”. All Twitter - The
Unofficial Twitter Resource, February 2013
[21] Mallon, Shanna. (2012). “50 Facts about Social Media for Business”. Straight North,
LLC publishing as The Straight North Blog. Downers Grove, IL.
[22] D. Karaiskos, et. al. (2010) “Social network addiction : a new clinical disorder?”. Eu-
ropean psychiatry : the journal of the Association of European Psychiatrists. volume
25, Page 855. DOI: 10.1016/S0924-9338(10)70846-4)
[23] Helms, R, Ignacio, et al.(2010) “Limitations of Network Analysis for Studying Efficiency
and Effectiveness of Knowledge Sharing” Electronic Journal of Knowledge Management
Volume 8 Issue 1 (pp53 - 68)
[24] Dhar, Vasant. (2013) “Data Science and Prediction”. Communications of the ACM.
Vol. 56 No 12, Pages 64-73. 10.1145/2500499
[25] Sullivan, Danny. (2011). “Why Second Chance Tweets MAtter: After 3 Hours, Few
Care About Socially Shared Links”. Thrid Door Media Inc. Publishing as Search Engine
Land.
[26] Travers J., Milgram S. (1969) “An Experimental Study of the Small World Problem,”
Sociometry, Vol. 32, No. 4. pp. 425-443, doi:10.2307/2786545
[27] Harrist, A. W., Zaia, A. F., Bates, J. E., Dodge, K. A. and Pettit, G. S. (1997).
“Subtypes of Social Withdrawal in Early Childhood: Sociometric Status and Social-
Cognitive Differences across Four Years”. Child Development, 68: 278âĂŞ294. doi:
10.1111/j.1467-8624.1997.tb01940.x
[28] Taylor, J. (2013). “Personal communication”. August 12, 2013.
[29] Galton, Antony. (2008). “Temporal Logic”. The Stanford En-
cyclopedia of Philosophy. Edward N. Zalta (ed.). URL =
http://plato.stanford.edu/archives/fall2008/entries/logic-temporal/.
[30] Minker, Jack. (1982). “On indefinite databases and the closed world assumption”. Lec-
ture Notes in Computer Science. 6th Conference on Automated Deduction. Springer
Berlind Heidelberg. pp. 292-308 doi=10.1007.BFb0000066
[31] Jeremy J. Carroll, Ian Dickinson, Chris Dollin, Dave Reynolds, Andy Seaborne,
and Kevin Wilkinson. (2004). “Jena: implementing the semantic web recommenda-
tions,” In Proceedings of the 13th international World Wide Web conference on Al-
ternate track papers & posters (WWW Alt.’04). ACM, New York, NY, USA, 74-83.
DOI=10.1145/1013367.1013381
[32] Claudio Gutierrez, et al. (2005) “Temporal RDF”. In Proceedings of the Second Euro-
pean conference on The Semantic Web: research and Applications (ESWC’05), Asun-
cion Gomez-Perez and Jerome Euzenat (Eds.). Springer-Verlag, Berlin, Heidelberg, 93-
107.
[33] Andrew Page.(2012). “Know Your Meme: Kony 2012”.
http://www.knowyourmeme.com/memes/events/kony-2012
[34] Goutam Kumar Saha. 2007. “Web ontology language (OWL) and seman-
tic web.” Ubiquity 2007, September, Article 1 (September 2007), 1 pages.
DOI=10.1145/1295289.1295290 http://doi.acm.org/10.1145/1295289.1295290
[35] John Guare, “Six Degrees of Seperation,” A Play, May 1990
[36] Lada Adamic, et al. (2003). “A social network caught in the Web,” First monday, 8(6)
[37] David Liben-Nowel, et al. (2005). “Geographic Routing in Social Networks,” Proceed-
ings of the National Academy of Sciences (PNAS), 102:11623-1162, 2005
[38] Ravi Kumar, et al. (2006). “Structure and Evolution of Online Social Networks,” In
the Proceedings of the 12th ACM SIGKDD International Conference on Knowledge
Discovery and Data Mininig (KDD;06), Philadelphia, PA.
[39] Michelle Girvan, Mark Newman. (2002). “Community structure in social and biologi-
cal networks,” Proceedings of the National Academy of Sciences (PNAS), 99(12):7821-
7826.
[40] Ceren Budak, et al. (2010). “Where the blogs tip: connectors, mavens, sales-
men and translators of the blogosphere”. In Proceedings of the First Workshop
on Social Media Analytics (SOMA ’10). ACM, New York, NY, USA, 106-114.
DOI=10.1145/1964858.1964873
[41] Steven Levitt, Stephen J. Dubner. (2005) “Freakonomics: A Rogue Economist Explores
the Hidden Side of Everything,” New York: Morrow-Harper.
[42] George Kelling, Catherine Coles. (1998). “Fixing Broken Windows: Restoring Order
and Reducing Crime in Our Communities,” January 20, 1998
[43] Roe v. Wade, 410 U.S. 113 (1973)
[44] Jonah Beger. (2013). “Contagious: Why Things Catch On,” Simon and Schuster
Publishing, March 5, 2013
[45] R. S. Renfro. (2001). “Modeling and Analysis of Social Networks’,’ PhD thesis, Air
Force Institute of Technology.
[46] C. Clark. (2005). “Modeling and analysis of clandestine networks,” Masters thesis, Air
Force Institute of Technology.
[47] J. T. Hamill. (2006). “Analysis of Layered Social Networks,” PhD thesis, Air Force
Institute of Technology.
[48] G. Ereteo , F. Gandon, M. Buffa, O. Corby. (2009) “Semantic Social Network Analysis,”
Proceedings of the WebSciâĂŹ09. http://journal.webscience.org/141/
| Clarkson University 16/28](https://image.slidesharecdn.com/6c7a31f0-7827-48ad-bc3c-45b325fdf4c7-141221131633-conversion-gate02/75/ase-social-informatics-6-16-2048.jpg)
![Initial Motivation
Partially inspired by Gladwell’s book, The Tipping Point [1], in which he discusses
how life can be thought of as an epidemic. Some criticism exists as to Gladwell’s
rigor, however for our use it is about inspiration and motivation not accuracy.
The Books Key Points “for our purposes”
• Actors (Connectors, Mavens, Salesmen).
• Information spreads like disease.
• Ideas reach a tipping point (critical mass).
Let’s Face It - Social Networks Are Fun
• We are a social species, that enjoy communicating and self adulation.
| Clarkson University 3/28](https://crownmelresort.com/image.slidesharecdn.com/6c7a31f0-7827-48ad-bc3c-45b325fdf4c7-141221131633-conversion-gate02/75/ase-social-informatics-6-3-2048.jpg)
![Actor Descriptions
• Isolate (Developmental Psychology) [27]
• Connector (Tipping Point) [1]
– Star (Small World Problem) [26]
– Bridge (The Hidden Organizational Chart) [2]
– Liason (The Hidden Organizational Chart) [2]
• Maven (Tipping Point) [1]
• Salesmen (Tipping Point) [1]
| Clarkson University 6/28](https://crownmelresort.com/image.slidesharecdn.com/6c7a31f0-7827-48ad-bc3c-45b325fdf4c7-141221131633-conversion-gate02/75/ase-social-informatics-6-6-2048.jpg)
![References
[1] Gladwell, M. (2000). “The tipping point”. Boston: Little, Brown and Company.
[2] Allen, H. T. (1976). “Communication networks - The hidden organizational chart”. The
Personnel Administrator, 21(6), 31-35.
[3] Arun Phadke, James Thorp. (1978). “Contracts and Influence”. Social Netowrks, 1:1-48
[4] Davis, A., et. al. (1941). “Deep South: A social Anthropological Study of Caste and
Class”. University of Chicago Press. Chicago, Ill.
[5] Freeman, L. (2004) “The Development of Social Network Analysis: A Study in the
Sociology of Science”. BookSurge, LLC. North Charleston, SC.
[6] Stanley Wasserman, Katherine Faust. (1994). “Social Network Analysis: Methods and
Applications”, Structural Analysis in the Social Sciences, 25 November 1994
[7] Donald Triner. (2010). “Publicaly Available Social Media Monitoring and Situational
Awareness Initiative,” Office of Operations Coordination and Planning: Departmetn of
Homeland Security, June 22 2010.
[8] Juris, Jeffrey. (2012). “reflections on #Occupy Everywhere: Social media, public space,
and emerging logics of aggregation”. American Ethnologist. Vol 39, No. 2, pp. 259-279.
[9] Sheedy, Caroline. (2011). “Social Media for Social Change: A Case Study of Social
Media Use in the 2011 Egyptian Revolution”. Capstone Project.
[10] Stark, Rodney. (1987). “Deviant Places: A Theory of the Ecology of Crime”. Criminol-
ogy, 25: 893âĂŞ910.
[11] Brett Stone-Gross, et al. (2011). “The underground economy of spam: a botmaster’s
perspective of coordinating large-scale spam campaigns.” In Proceedings of the 4th
USENIX conference on Large-scale exploits and emergent threats (LEET’11). USENIX
Association, Berkeley, CA, USA, 4-4.
[12] Taylor Dewey, et al. (2012). “The Impact of Social Media on Social Unrest in the Arab
Spring”. Stanford University - Defense Intelligence Agency Final Report.
[13] Christian Sturm and Hossam Amer. (2013). “The effects of (social) media on rev-
olutions: perspectives from egypt and the arab spring”. In Proceedings of the 15th
international conference on Human-Computer Interaction: users and contexts of use -
Volume Part III (HCI’13), Masaaki Kurosu (Ed.), Vol. Part III. Springer-Verlag, Berlin,
Heidelberg, 352-358.
[14] Woods, Richard. (2010). “Privacy is Dead?: Facebook’s Mark Zuckerberg says privacy
is dead. So why does he want to keeps this picture hidden?”. Times Newspapers Ltd.
[15] Statistics Brain. (2013). “’Facebook Statistics”. Statistic Brain Research Institute,
publishing as Statistic Brain. 6/23/2013. http://www.statisticbrain.com/facebook-
statistics/
[16] CBS News. (2012). “Twitter’s censorship plan rouses global furor”. Associated Press.
January 27, 2012
[17] Statistics Brain. (2013). “Twitter Statistics”. Statistic Brain Research Institute, pub-
lishing as Statistic Brain. 5/7/2013. http://www.statisticbrain.com/twitter-statistics/
[18] Bagley, Nick. (2012). “The Decline of Myspace: Future of Social Media”. Dream-
grow Digital. 8/13/2012. http://www.dreamgrow.com/the-decline-of-myspace-future-
of-social-media/
[19] alton, Antony, “Temporal Logic”, The Stanford Encyclopedia of Philosophy (Fall 2008
Edition), Edward N. Zalta (ed.)
[20] Shea Bennett. “Just How Big Is twitter In 2012 [INFOGRAPHIC]”. All Twitter - The
Unofficial Twitter Resource, February 2013
[21] Mallon, Shanna. (2012). “50 Facts about Social Media for Business”. Straight North,
LLC publishing as The Straight North Blog. Downers Grove, IL.
[22] D. Karaiskos, et. al. (2010) “Social network addiction : a new clinical disorder?”. Eu-
ropean psychiatry : the journal of the Association of European Psychiatrists. volume
25, Page 855. DOI: 10.1016/S0924-9338(10)70846-4)
[23] Helms, R, Ignacio, et al.(2010) “Limitations of Network Analysis for Studying Efficiency
and Effectiveness of Knowledge Sharing” Electronic Journal of Knowledge Management
Volume 8 Issue 1 (pp53 - 68)
[24] Dhar, Vasant. (2013) “Data Science and Prediction”. Communications of the ACM.
Vol. 56 No 12, Pages 64-73. 10.1145/2500499
[25] Sullivan, Danny. (2011). “Why Second Chance Tweets MAtter: After 3 Hours, Few
Care About Socially Shared Links”. Thrid Door Media Inc. Publishing as Search Engine
Land.
[26] Travers J., Milgram S. (1969) “An Experimental Study of the Small World Problem,”
Sociometry, Vol. 32, No. 4. pp. 425-443, doi:10.2307/2786545
[27] Harrist, A. W., Zaia, A. F., Bates, J. E., Dodge, K. A. and Pettit, G. S. (1997).
“Subtypes of Social Withdrawal in Early Childhood: Sociometric Status and Social-
Cognitive Differences across Four Years”. Child Development, 68: 278âĂŞ294. doi:
10.1111/j.1467-8624.1997.tb01940.x
[28] Taylor, J. (2013). “Personal communication”. August 12, 2013.
[29] Galton, Antony. (2008). “Temporal Logic”. The Stanford En-
cyclopedia of Philosophy. Edward N. Zalta (ed.). URL =
http://plato.stanford.edu/archives/fall2008/entries/logic-temporal/.
[30] Minker, Jack. (1982). “On indefinite databases and the closed world assumption”. Lec-
ture Notes in Computer Science. 6th Conference on Automated Deduction. Springer
Berlind Heidelberg. pp. 292-308 doi=10.1007.BFb0000066
[31] Jeremy J. Carroll, Ian Dickinson, Chris Dollin, Dave Reynolds, Andy Seaborne,
and Kevin Wilkinson. (2004). “Jena: implementing the semantic web recommenda-
tions,” In Proceedings of the 13th international World Wide Web conference on Al-
ternate track papers & posters (WWW Alt.’04). ACM, New York, NY, USA, 74-83.
DOI=10.1145/1013367.1013381
[32] Claudio Gutierrez, et al. (2005) “Temporal RDF”. In Proceedings of the Second Euro-
pean conference on The Semantic Web: research and Applications (ESWC’05), Asun-
cion Gomez-Perez and Jerome Euzenat (Eds.). Springer-Verlag, Berlin, Heidelberg, 93-
107.
[33] Andrew Page.(2012). “Know Your Meme: Kony 2012”.
http://www.knowyourmeme.com/memes/events/kony-2012
[34] Goutam Kumar Saha. 2007. “Web ontology language (OWL) and seman-
tic web.” Ubiquity 2007, September, Article 1 (September 2007), 1 pages.
DOI=10.1145/1295289.1295290 http://doi.acm.org/10.1145/1295289.1295290
[35] John Guare, “Six Degrees of Seperation,” A Play, May 1990
[36] Lada Adamic, et al. (2003). “A social network caught in the Web,” First monday, 8(6)
[37] David Liben-Nowel, et al. (2005). “Geographic Routing in Social Networks,” Proceed-
ings of the National Academy of Sciences (PNAS), 102:11623-1162, 2005
[38] Ravi Kumar, et al. (2006). “Structure and Evolution of Online Social Networks,” In
the Proceedings of the 12th ACM SIGKDD International Conference on Knowledge
Discovery and Data Mininig (KDD;06), Philadelphia, PA.
[39] Michelle Girvan, Mark Newman. (2002). “Community structure in social and biologi-
cal networks,” Proceedings of the National Academy of Sciences (PNAS), 99(12):7821-
7826.
[40] Ceren Budak, et al. (2010). “Where the blogs tip: connectors, mavens, sales-
men and translators of the blogosphere”. In Proceedings of the First Workshop
on Social Media Analytics (SOMA ’10). ACM, New York, NY, USA, 106-114.
DOI=10.1145/1964858.1964873
[41] Steven Levitt, Stephen J. Dubner. (2005) “Freakonomics: A Rogue Economist Explores
the Hidden Side of Everything,” New York: Morrow-Harper.
[42] George Kelling, Catherine Coles. (1998). “Fixing Broken Windows: Restoring Order
and Reducing Crime in Our Communities,” January 20, 1998
[43] Roe v. Wade, 410 U.S. 113 (1973)
[44] Jonah Beger. (2013). “Contagious: Why Things Catch On,” Simon and Schuster
Publishing, March 5, 2013
[45] R. S. Renfro. (2001). “Modeling and Analysis of Social Networks’,’ PhD thesis, Air
Force Institute of Technology.
[46] C. Clark. (2005). “Modeling and analysis of clandestine networks,” Masters thesis, Air
Force Institute of Technology.
[47] J. T. Hamill. (2006). “Analysis of Layered Social Networks,” PhD thesis, Air Force
Institute of Technology.
[48] G. Ereteo , F. Gandon, M. Buffa, O. Corby. (2009) “Semantic Social Network Analysis,”
Proceedings of the WebSciâĂŹ09. http://journal.webscience.org/141/
| Clarkson University 16/28](https://crownmelresort.com/image.slidesharecdn.com/6c7a31f0-7827-48ad-bc3c-45b325fdf4c7-141221131633-conversion-gate02/75/ase-social-informatics-6-16-2048.jpg)
Uploaded byJoshua S. White, PhD josh@securemind.org
ase-social-informatics (6)
The document describes a method for analyzing online social networks using actor-based network analysis and tenses predicate logic. It discusses classifying users into different actor types like connectors, mavens, and salesmen based on their behaviors and roles in spreading information. The method is demonstrated on a large Twitter dataset to analyze how the #KONY2012 topic spread. Future work is discussed to expand the approach with semantic analysis and develop an ontology to integrate actor and event analysis.
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Similar to ase-social-informatics (6)
ase-social-informatics (6)
- 1. Connectors, Mavens, Salesmenand More: An Actor-Based Online Social Network Analysis Method Using Tensed Predicate Logic Joshua S. White, PhD Department of Computer Science State University of New York Polytechnic Institute Jeanna N. Matthews, PhD Department of Computer Science Clarkson University ASE SocialInformatics2014 December 16, 2014 | Clarkson University 1/28
- 2. Outline Initial Motivation .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Problem Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Actor Descriptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Actor Identification Example: Liaison . . . . . . . . . . . . . . . . . . . . . . . . . 7 Established Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 Actor Identification Example: Results . . . . . . . . . . . . . . . . . . . . . . . . . 13 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 Contact . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 Suplimental Material . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 | Clarkson University 2/28
- 3. Initial Motivation Partially inspiredby Gladwell’s book, The Tipping Point [1], in which he discusses how life can be thought of as an epidemic. Some criticism exists as to Gladwell’s rigor, however for our use it is about inspiration and motivation not accuracy. The Books Key Points “for our purposes” • Actors (Connectors, Mavens, Salesmen). • Information spreads like disease. • Ideas reach a tipping point (critical mass). Let’s Face It - Social Networks Are Fun • We are a social species, that enjoy communicating and self adulation. | Clarkson University 3/28
- 4. Problem Questions • Arethere information security applications for social network data-mining? ! Can we detect malicious social network use? ! Can we analyze the spread of a major malware campaign? 9 Can we detect phishing in near-real-time • Can we determine how information spreads on these networks? 9 Can we determine if a user is unique? 8 Is there a way of classifying users based on actor types? 9 Can we determine who the opinion leaders or influencers are? | Clarkson University 4/28
- 5.
- 6. Actor Descriptions • Isolate(Developmental Psychology) [27] • Connector (Tipping Point) [1] – Star (Small World Problem) [26] – Bridge (The Hidden Organizational Chart) [2] – Liason (The Hidden Organizational Chart) [2] • Maven (Tipping Point) [1] • Salesmen (Tipping Point) [1] | Clarkson University 6/28
- 7. Actor Identification Example:Liaison • Liaison: (Noun not Verb) – A person (b) who connects party 1 (a) and party 2 (c) through a requested introduction. – Like requesting for a first level contact on Linkedin to introduce you to someone in their network • Not all social networks have a special features like Linkedin, we need to derive this relationship... Time is important! • Previous methods did not take event sequence into account | Clarkson University 7/28
- 8. Actor (b): Liaison- Logical For the graph (a,b,c), It will at some time be the case that edge (a,b) exists and It will at some time be the case that edge (b,c) exists and It will at some time be the case that edge (c,a) exists and It has always been the case that edge (c,a) did not exist. | Clarkson University 8/28
- 9. Actor Identification Example:Liaison | Clarkson University 9/28
- 10. Actor Identification Continued |Clarkson University 10/28
- 11. Actor Identification SampleLogics | Clarkson University 11/28
- 12. Established Dataset • In2012 we collected 165 TB of Twitter Data (Uncompressed) – 175 Days Collected, 147 Full Days ∗ Estimated 45 Billion Tweets – Estimates place total Twitter traffic at 175 million tweets/day-2012 – Daily collection rates between 50% and 80% of total traffic | Clarkson University 12/28
- 13. Actor Identification Example:Results • Remember those pretty plots from earilier? • We take our entire dataset and filter it for 31 days between February 20th and March 20th, and for only #KONY2012 related Tweets | Clarkson University 13/28
- 14. Conclusions • We aimedto answer the following subset of questions when we started this portion of our work: – Can we come up with a way of classifying users based on actor types? – Can we determine who the opinion leaders or influencers are? – Can we determine how information spreads on these networks? | Clarkson University 14/28
- 15. Future Work • Wehave established a more perminant test facility and dataset location in the COSI (Clarkson Open Source Institute) • We are pursuing the semantic side of social network analysis – Currently only one true SNA semantic ontology exists that is openly available and it’s only on paper. – We are planning on rolling both the actor and event analysis into one approach which will be part of a new ontology • We have grown our team to include a number of individuals affliated with multiple institutions. • We recently finished a project using machine learning to process URLs and web-pages on-mass to detect Phishing • We recently finished a project that analyzed Twitter accounts for duplication, or single ownership | Clarkson University 15/28
- 16. References [1] Gladwell, M.(2000). “The tipping point”. Boston: Little, Brown and Company. [2] Allen, H. T. (1976). “Communication networks - The hidden organizational chart”. The Personnel Administrator, 21(6), 31-35. [3] Arun Phadke, James Thorp. (1978). “Contracts and Influence”. Social Netowrks, 1:1-48 [4] Davis, A., et. al. (1941). “Deep South: A social Anthropological Study of Caste and Class”. University of Chicago Press. Chicago, Ill. [5] Freeman, L. (2004) “The Development of Social Network Analysis: A Study in the Sociology of Science”. BookSurge, LLC. North Charleston, SC. [6] Stanley Wasserman, Katherine Faust. (1994). “Social Network Analysis: Methods and Applications”, Structural Analysis in the Social Sciences, 25 November 1994 [7] Donald Triner. (2010). “Publicaly Available Social Media Monitoring and Situational Awareness Initiative,” Office of Operations Coordination and Planning: Departmetn of Homeland Security, June 22 2010. [8] Juris, Jeffrey. 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(2013). “Contagious: Why Things Catch On,” Simon and Schuster Publishing, March 5, 2013 [45] R. S. Renfro. (2001). “Modeling and Analysis of Social Networks’,’ PhD thesis, Air Force Institute of Technology. [46] C. Clark. (2005). “Modeling and analysis of clandestine networks,” Masters thesis, Air Force Institute of Technology. [47] J. T. Hamill. (2006). “Analysis of Layered Social Networks,” PhD thesis, Air Force Institute of Technology. [48] G. Ereteo , F. Gandon, M. Buffa, O. Corby. (2009) “Semantic Social Network Analysis,” Proceedings of the WebSciâĂŹ09. http://journal.webscience.org/141/ | Clarkson University 16/28
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- 19. Suplimental Material | ClarksonUniversity 19/28
- 20. • Twitter JSONKey Fields profile_link_color Coordinates verified In_reply_to_screen_name Geo time_zone In_reply_to_status_id text statuses_count In_reply_to_status_id_str entities Contributors In_reply_to_user_id place protected profile_background_color contributors_enabled trunkated profile_background_title default_profile retweeted default_profile_image description id_translator follow_request_sent followers_count location friends_count geo_endabled favorites_count profile_image_url_https listed_count following profile_background_image_url notifications retweet_count background_image_url_https name created_at profile_image_url lang Favorited sidebar_border_color use_background_image Id_str sidebar_fill_color screen_name Created_at profile_text_color show_all_inline_media Id url utc_offset | Clarkson University 20/28
- 21. • BEK InfectiousAccount Visualization | Clarkson University 21/28
- 22. • Coalmine UserInterface | Clarkson University 22/28
- 23. • Malware InfectionVector Detection Continued | Clarkson University 23/28
- 24. • Malware InfectionVector Detection Continued | Clarkson University 24/28
- 25. Event Identification • Stillin the initial stages of this part of our work • Given a general topic, “search term, hashtag,” we can identify most of the related content from the dataset • We have a means for alerting on all new posts regarding that term • We can dig historically through the data and trace the path that an itea took • We can identify the influential individuals, “accounts,” that played a part in the information spread • Our test case was the KONY2012 Event | Clarkson University 25/28
- 26. Event Identification Continued |Clarkson University 26/28
- 27. Event Identification Continued •Top 10 Twitter Accounts, sending and receiving KONY2012 related Tweets Directed @ Account Names In-Degree Origin Account Names Out-Degree tothekidswho 625 twittonpeace 47 Invisible 125 interhabernet 44 youtube 118 DailyisOut 44 helpspreadthis 95 MEDYA_TURK 42 justinbieber 83 haber_42 35 prettypinkprobz 48 gundem_haber 30 ninadobrev 48 twittofpeace 22 MeekMill 47 korkmazhaber 19 ladygaga 43 tarafsiz_haber 14 KendallJenner 39 Son_DakikaHaber 13 | Clarkson University 27/28
- 28. Event Identification Continued •Top 10 Twitter Accounts, retweeting and being retweeted regarding KONY2012 Retweeting Accounts In-Degree Message Source Out-Degree MedyaKonya 8 Stop____Kony 2642 twittonpeace 8 tothekidswho 753 haber_42 7 konyfamous2012 716 gundem_haber 7 Kony2012Help 615 korkmazhaber 7 stop______kony 353 DailyisOut 7 WESTOPKONY 225 interhabernet 6 zaynmalik 221 KONYA_ZAMAN 6 iSayStopKony 127 konya_time 6 Stop_2012_Kony 80 konyagazetesi 5 Kony_Awareness 72 | Clarkson University 28/28