The document discusses an experimental study comparing local and global trust metrics on the Epinions.com review community. It finds that around 17,090 users (over 20%) on Epinions are considered controversial, having at least one person who disagrees with the majority view of them. Experiments show that local trust metrics like MoleTrust2 perform better than global metrics like eBay's at predicting distrust relationships for controversial users, though error rates remain higher for these users. Further experiments vary propagation horizons and evaluate coverage. The study concludes local metrics are generally better suited to online communities where opinions of individuals can vary significantly.
Presentazione di Paolo Massa nell'ambito del Seminario residenziale “L’approccio territoriale tra aiuto e crescita” - 22-23 giugno 2012 - Villa Flangini - Asolo - Organizzato dal SerAT (Servizio Alcologia e Tabagismo Ulss 8)
Con il contributo di ACAT-ULSS 8 onlus e Cooperativa Sonda. Con il patrocinio di Alcologia Ecologica
Manypedia: Comparing Language Points of View of Wikipedia CommunitiesPaolo Massa
Manypedia is at http://www.manypedia.com
These slides have been presented by Paolo Massa at WikiSym, 8th International Symposium on Wikis and Open Collaboration, 29 August 2013, Linz, Austria.
Manypedia is joint work of Paolo Massa and Federico Scrinzi (and it is open source too!)
The paper is at http://www.gnuband.org/papers/manypedia-comparing-language-points-of-view-of-wikipedia-communities/
If you like Manypedia and you have a chance, don't forget to cite our paper, thanks!
Transcendent Interactions Collaborative Contexts and Relationship-based Compu...Paolo Massa
Not a presentation by me
BUT
A presentation
by
Stewart Butterfield
Ben Cerveny
Eric Costello
Ludicorp Ltd.
(I was always referring to it, so now there\'s a URL to refer to ;)
Presentazione di Paolo Massa nell'ambito del Seminario residenziale “L’approccio territoriale tra aiuto e crescita” - 22-23 giugno 2012 - Villa Flangini - Asolo - Organizzato dal SerAT (Servizio Alcologia e Tabagismo Ulss 8)
Con il contributo di ACAT-ULSS 8 onlus e Cooperativa Sonda. Con il patrocinio di Alcologia Ecologica
Manypedia: Comparing Language Points of View of Wikipedia CommunitiesPaolo Massa
Manypedia is at http://www.manypedia.com
These slides have been presented by Paolo Massa at WikiSym, 8th International Symposium on Wikis and Open Collaboration, 29 August 2013, Linz, Austria.
Manypedia is joint work of Paolo Massa and Federico Scrinzi (and it is open source too!)
The paper is at http://www.gnuband.org/papers/manypedia-comparing-language-points-of-view-of-wikipedia-communities/
If you like Manypedia and you have a chance, don't forget to cite our paper, thanks!
Transcendent Interactions Collaborative Contexts and Relationship-based Compu...Paolo Massa
Not a presentation by me
BUT
A presentation
by
Stewart Butterfield
Ben Cerveny
Eric Costello
Ludicorp Ltd.
(I was always referring to it, so now there\'s a URL to refer to ;)
Trustlet, Open Research on Trust MetricsPaolo Massa
Presentation of a paper at 11th Business Information Systems conference - 2nd Workshop on Social Aspects of the Web (SAW 2008).
Trustlet.org is a cooperative environment (wiki) for research of trust metrics on social networks, sharing of social network datasets and of trust metrics code as Free Software
Come hear about some of the new challenges with measuring social media marketing including word-of-mouth, comments on blogs, connections in social networks, questions in support forums, and tags on online media. Learn about where to focus online marketing efforts as the social Web grows in importance over the coming years.
News Source Credibility in the Eyes of Different AssessorsMartino Mensio
Presentation at the Truth and Trust Online Conference of the paper http://oro.open.ac.uk/62771/
Abstract:
With misinformation being one of the biggest issues of current times, many organisations are emerging to offer verifications of information and assessments of news sources. However, it remains unclear how they relate in terms of coverage, overlap and agreement. In this paper we introduce a comparison of the assessments produced by different organisations, in order to measure their overlap and agreement on news sources. Relying on the general term of credibility, we map each of the different assessments to a unified scale. Then we compare two different levels of credibility assessments (source level and document level) by using the data published by various organisations, including fact-checkers, to see which sources they assess more than others, how much overlap there is between them, and how much agreement there is between their verdicts. Our results show that the overlap between the different origins is generally quite low, meaning that different experts and tools provide evaluations for a rather disjoint set of sources, also when considering fact-checking. For agreement, instead we find that there are origins that agree more than others on the verdicts.
Inter-context Trust Bootstrapping for Mobile Content SharingDaniele Quercia
This talk will look at how a trust model running on device A determines the extent to which A should initially trust device B in a given context (content category). It does so by considering two cases: in the first, A does not know B at all; in the second case, A knows B but in contexts other than that of interest. For each of those two cases, this talk will discuss the most recent proposal that improves on existing solutions (TRULLO and distributed propagation), and will also attempt to suggest new research directions (such as private collaborative filtering - post & more).
Presented at #H2OWorld 2017 in Mountain View, CA.
Enjoy the video: https://youtu.be/TBJqgvXYhfo.
Learn more about H2O.ai: https://www.h2o.ai/.
Follow @h2oai: https://twitter.com/h2oai.
- - -
Abstract:
Machine learning is at the forefront of many recent advances in science and technology, enabled in part by the sophisticated models and algorithms that have been recently introduced. However, as a consequence of this complexity, machine learning essentially acts as a black-box as far as users are concerned, making it incredibly difficult to understand, predict, or "trust" their behavior. In this talk, I will describe our research on approaches that explain the predictions of ANY classifier in an interpretable and faithful manner.
Sameer's Bio:
Dr. Sameer Singh is an Assistant Professor of Computer Science at the University of California, Irvine. He is working on large-scale and interpretable machine learning applied to natural language processing. Sameer was a Postdoctoral Research Associate at the University of Washington and received his PhD from the University of Massachusetts, Amherst, during which he also worked at Microsoft Research, Google Research, and Yahoo! Labs on massive-scale machine learning. He was awarded the Adobe Research Data Science Faculty Award, was selected as a DARPA Riser, won the grand prize in the Yelp dataset challenge, and received the Yahoo! Key Scientific Challenges fellowship. Sameer has published extensively at top-tier machine learning and natural language processing conferences. (http://sameersingh.org)
Gaming the Social: Community, Measurement & MonetizationSuperData
This lecture will show how the dynamics of online audiences can be harnessed and discuss essential components of a sustainable online entertainment business. Perhaps more so than traditional media and entertainment firms, a direct interaction with their customer base allows online game companies to cultivate loyalty and long-term profitability. To do this effectively, however, requires an ongoing effort to understand audience preferences and behaviors that go beyond the usual surface-level market research. Here, I will present several key insights into online communities and how these may fit into a larger strategic approach.
How to Use Social Media To Attract More Customers - HubSpotHubSpot
Businesses now have the power to leverage the Internet -- search engines, blogs, social media -- to reach customers more effectively. This includes connecting with customers where they hang out online and engaging in conversations about the topics most important to them. Social CRM (Customer Relationship Management) is all about joining the ongoing conversations our customers and prospects are already having and not trying to control them. It's realizing that people like doing business with people they like and love doing business with people they trust.
This presentation covers:
* How to use social media to connect with customers online
* Creating content to attract more customers to your business
* Tools to help you manage and measure your social media efforts
DESIGN PRINCIPLES OF WIKIS AND THEIR IMPACT ON KNOWLEDGE EXCHANGE PROCESSES Paolo Massa
DESIGN PRINCIPLES OF WIKIS AND THEIR IMPACT ON KNOWLEDGE EXCHANGE PROCESSES
From Analyzing Wiki-based Networks to Improve Knowledge Processes in Organizations by Claudia Müller, Benedikt Meuthrath, Anne Baumgraß Slides by Paolo Massa
Trustlet, Open Research on Trust MetricsPaolo Massa
Presentation of a paper at 11th Business Information Systems conference - 2nd Workshop on Social Aspects of the Web (SAW 2008).
Trustlet.org is a cooperative environment (wiki) for research of trust metrics on social networks, sharing of social network datasets and of trust metrics code as Free Software
Come hear about some of the new challenges with measuring social media marketing including word-of-mouth, comments on blogs, connections in social networks, questions in support forums, and tags on online media. Learn about where to focus online marketing efforts as the social Web grows in importance over the coming years.
News Source Credibility in the Eyes of Different AssessorsMartino Mensio
Presentation at the Truth and Trust Online Conference of the paper http://oro.open.ac.uk/62771/
Abstract:
With misinformation being one of the biggest issues of current times, many organisations are emerging to offer verifications of information and assessments of news sources. However, it remains unclear how they relate in terms of coverage, overlap and agreement. In this paper we introduce a comparison of the assessments produced by different organisations, in order to measure their overlap and agreement on news sources. Relying on the general term of credibility, we map each of the different assessments to a unified scale. Then we compare two different levels of credibility assessments (source level and document level) by using the data published by various organisations, including fact-checkers, to see which sources they assess more than others, how much overlap there is between them, and how much agreement there is between their verdicts. Our results show that the overlap between the different origins is generally quite low, meaning that different experts and tools provide evaluations for a rather disjoint set of sources, also when considering fact-checking. For agreement, instead we find that there are origins that agree more than others on the verdicts.
Inter-context Trust Bootstrapping for Mobile Content SharingDaniele Quercia
This talk will look at how a trust model running on device A determines the extent to which A should initially trust device B in a given context (content category). It does so by considering two cases: in the first, A does not know B at all; in the second case, A knows B but in contexts other than that of interest. For each of those two cases, this talk will discuss the most recent proposal that improves on existing solutions (TRULLO and distributed propagation), and will also attempt to suggest new research directions (such as private collaborative filtering - post & more).
Presented at #H2OWorld 2017 in Mountain View, CA.
Enjoy the video: https://youtu.be/TBJqgvXYhfo.
Learn more about H2O.ai: https://www.h2o.ai/.
Follow @h2oai: https://twitter.com/h2oai.
- - -
Abstract:
Machine learning is at the forefront of many recent advances in science and technology, enabled in part by the sophisticated models and algorithms that have been recently introduced. However, as a consequence of this complexity, machine learning essentially acts as a black-box as far as users are concerned, making it incredibly difficult to understand, predict, or "trust" their behavior. In this talk, I will describe our research on approaches that explain the predictions of ANY classifier in an interpretable and faithful manner.
Sameer's Bio:
Dr. Sameer Singh is an Assistant Professor of Computer Science at the University of California, Irvine. He is working on large-scale and interpretable machine learning applied to natural language processing. Sameer was a Postdoctoral Research Associate at the University of Washington and received his PhD from the University of Massachusetts, Amherst, during which he also worked at Microsoft Research, Google Research, and Yahoo! Labs on massive-scale machine learning. He was awarded the Adobe Research Data Science Faculty Award, was selected as a DARPA Riser, won the grand prize in the Yelp dataset challenge, and received the Yahoo! Key Scientific Challenges fellowship. Sameer has published extensively at top-tier machine learning and natural language processing conferences. (http://sameersingh.org)
Gaming the Social: Community, Measurement & MonetizationSuperData
This lecture will show how the dynamics of online audiences can be harnessed and discuss essential components of a sustainable online entertainment business. Perhaps more so than traditional media and entertainment firms, a direct interaction with their customer base allows online game companies to cultivate loyalty and long-term profitability. To do this effectively, however, requires an ongoing effort to understand audience preferences and behaviors that go beyond the usual surface-level market research. Here, I will present several key insights into online communities and how these may fit into a larger strategic approach.
How to Use Social Media To Attract More Customers - HubSpotHubSpot
Businesses now have the power to leverage the Internet -- search engines, blogs, social media -- to reach customers more effectively. This includes connecting with customers where they hang out online and engaging in conversations about the topics most important to them. Social CRM (Customer Relationship Management) is all about joining the ongoing conversations our customers and prospects are already having and not trying to control them. It's realizing that people like doing business with people they like and love doing business with people they trust.
This presentation covers:
* How to use social media to connect with customers online
* Creating content to attract more customers to your business
* Tools to help you manage and measure your social media efforts
Similar to Controversial Users demand Local Trust Metrics: an Experimental Study on Epinions.com Community (19)
DESIGN PRINCIPLES OF WIKIS AND THEIR IMPACT ON KNOWLEDGE EXCHANGE PROCESSES Paolo Massa
DESIGN PRINCIPLES OF WIKIS AND THEIR IMPACT ON KNOWLEDGE EXCHANGE PROCESSES
From Analyzing Wiki-based Networks to Improve Knowledge Processes in Organizations by Claudia Müller, Benedikt Meuthrath, Anne Baumgraß Slides by Paolo Massa
Collective Memory building in Wikipedia: the case of North African uprisingsPaolo Massa
Paper presented at Wikisym 2011, 7th International Symposium on Wikis and Open Collaboration
Read the paper at http://www.gnuband.org/papers/collective_memory_building_in_wikipedia_the_case_of_north_african_uprisings/
Authors: Michela Ferron, Paolo Massa
Abstract:
Since December 2010, a series of protests and uprisings have shocked North African countries such as Tunisia, Egypt, Libya, Syria, Yemen and more. In this paper, focusing mainly on the Egyptian revolution, we provide evidence of the intense edit activity occurred during these uprisings on the related Wikipedia
pages. Thousands of people provided their contribution on the content pages and discussed improvements and disagreements on the associated talk pages as the traumatic events unfolded. We
propose to interpret this phenomenon as a process of collective memory building and argue how on Wikipedia this can be studied empirically and quantitatively in real time. We explore and suggest possible directions for future research on collective memory formation of traumatic and controversial events in Wikipedia.
Social networks of Wikipedia - Paolo Massa - Presentation at (2011). ACM Hype...Paolo Massa
The paper is at http://www.gnuband.org/papers/social_networks_of_wikipedia/
Wikipedia, the free online encyclopedia anyone can edit, is a live social experiment: millions of individuals volunteer their knowledge and time to collective create it. It is hence interesting trying to understand how they do it. While most of the attention concentrated on article pages, a less known share of activities happen on user talk pages, Wikipedia pages where a message can be left for the specific user. This public conversations can be studied from a Social Network Analysis perspective in order to highlight the structure of the “talk” network. In this paper we focus on this preliminary extraction step by proposing different algorithms. We then empirically validate the differences in the networks they generate on the Venetian Wikipedia with the real network of conversations extracted manually by coding every message left on all user talk pages. The comparisons show that both the algorithms and the manual process contain inaccuracies that are intrinsic in the freedom and unpredictability of Wikipedia growth. Nevertheless, a precise description of the involved issues allows to make informed decisions and to base empirical findings on reproducible evidence. Our goal is to lay the foundation for a solid computational sociology of wikis. For this reason we release the scripts encoding our algorithms as open source and also some datasets extracted out of Wikipedia conversations, in order to let other researchers replicate and improve our initial effort.
Scripts (Python) has been released as open source and networks datasets (in GraphML format) too. See http://sonetlab.fbk.eu/data/social_networks_of_wikipedia/
An Empirical Analysis on Social Capital and Enterprise 2.0 Participation in a...Paolo Massa
An Empirical Analysis on Social Capital and Enterprise 2.0 Participation in a Research Institute
by
Ferron Michela, Frassoni Marco, Massa Paolo, Napolitano Maurizio, Setti Davide
SoNet project - Fondazione Bruno Kessler (FBK) - Trento, Italy
http://sonet.fbk.eu
2010 International Conference on Advances in Social Networks Analysis and Mining
Odense, Denmark
August 09-August 11
ISBN: 978-0-7695-4138-9
The paper is at http://www.gnuband.org/papers/an_empirical_analysis_on_social_capital_and_enterprise_20_participation_in_a_research_institute
Supporting Collaborative Networks in Organizational Settings using an Enterpr...Paolo Massa
Presentation of the paper "Supporting Collaborative Networks in Organizational Settings using an Enterprise 2.0 platform" at NETSCI 09 International Workshop and Conference on Complex Networks and their Applications, Venezia, Italy. July 2009
The paper is at http://www.gnuband.org/papers/supporting_collaborative_networks_in_organizational_settings_using_an_enterprise_20_platform/
The Future of Work, Fun, and Being Social: an introduction to the nascent adv...Paolo Massa
How Internet Reputation Systems and
The Online Coordination of Offline Life are
Changing the Fundamental Structure of Society
v1.0 28 Feb 2007 Joe Edelman <joe>
on
CouchSurfing Int’l & Emergency Communities
CC-SA-BY
Feedback Effects Between Similarity And Social Influence In Online CommunitiesPaolo Massa
SoNet Research Meeting presentation
Feedback Effects Between Similarity And Social Influence In Online Communities.
Authors: David Crandall, Dan Cosley, Daniel Huttenlocher, Jon Kleinberg, Siddharth Suri
Cornell University Ithaca, NY
2008 KDD: Proceeding of the 14th ACM KDD international conference on Knowledge discovery and data mining
#citations at 2010/04/09 from Google Scholar:44
Presenter: Paolo Massa, SoNet group, http://sonet.fbk.eu
Bowling Alone and Trust Decline in Social Network SitesPaolo Massa
In this paper we analyze the community of a social network site, Advogato. The peculiar characteristics of Advogato is that users can explicitly express weighted trust relationships among themselves. We conduct a longitudinal analysis of the trust network over a time period of 4 years, exploring the community as it grew from a knit circle of 300 users to an society of almost 6500 individuals. We report the changes over time of standard indexes in social network analysis such as clustering and degrees of separation. We then focus on specific measures about trust such as reciprocity and changes over time of average trust. A decline in trust is observed as the community grows. Following what we believe to be the first empirical analysis of trust evolution over time in a real community, we conclude suggesting how the availability of data about human relationships in social network sites is opening up the possibility of monitoring changes in trust in real time. In order to foster this research line, we released the datasets and the code we used in our analysis.
Fukuyama' trust - The role of trust and trust networks in the societyPaolo Massa
Presentation about how Fukuyama describes the concept of trust. NOT A PRESENTATION CREATED BY ME, I just placed it on slideshare in order to embed it in my blog.
The Power of Social Media (Ricardo Baeza-Yates)Paolo Massa
The Power of Social Media. Slides presented by Ricardo Baeza-Yates, director of the Yahoo! Research labs at Barcelona, Spain and Santiago, Chile, during the public kickoff of the LiveMemories project http://www.livememories.org
A lecture I gave for the course "Linux per tutti, tutti per GNU/Linux" about the importance of Free Software (and open standards) for the future of our common Internet and Web.
Course site http://trentowiki.it/ISFGNULinux
Welcome to the Program Your Destiny course. In this course, we will be learning the technology of personal transformation, neuroassociative conditioning (NAC) as pioneered by Tony Robbins. NAC is used to deprogram negative neuroassociations that are causing approach avoidance and instead reprogram yourself with positive neuroassociations that lead to being approach automatic. In doing so, you change your destiny, moving towards unlocking the hypersocial self within, the true self free from fear and operating from a place of personal power and love.
Ethical_dilemmas_MDI_Gurgaon-Business Ethics Case 1.pptx
Controversial Users demand Local Trust Metrics: an Experimental Study on Epinions.com Community
1. Controversial Users demand
Local Trust Metrics: an
Experimental
Study on Epinions.com
Community
Paolo Massa
PhD student in ICT
ITC/iRST and University of Trento
Blog: http://moloko.itc.it/paoloblog/
(Joint Work with Paolo Avesani)
(Thanks Epinions.com for providing data)
Slides licenced under CreativeCommons AttributionShareAlike (see last slide for more info)
3. Motivation
In a society, some peers are unknown to you (ebay,
p2p, ...)
Q: “Should I trust peer A?” [decentralization>relevant]
Most papers assume a peer has a unique quality value
(there are good peers and bad peers, goal is to spot bad)
IRREALISTIC assumption (Evidence from real online
community of 150.000 users).
Consequence: we need Local Trust Metrics
(personalized) [But most papers propose Global Metrics]
4. Epinions.com
What is Epinions.com?
Community web site where users can
Write reviews about items and give them ratings
Express their Web of Trust (“Users whose reviews and
ratings you have consistently found to be valuable”)
Express their Block List (“Users whose reviews and
ratings ... offensive, inaccurate, or in general not valuable”)
Reviews of TRUSTed users are more visible
Reviews of DISTRUSTed users are hidden
5. Epinions.com
Dr.P profile page
Dr.P's Web of Trust
(Block List is hidden)
Do you
trust or distrust Dr.P?
Ratings given by Dr.P
6. Real uses of Trust
News sites: Slashdot.org, Kuro5hin.org, ...
Emarketplaces: Ebay.com, Epinions.com, Amazon.com, ...
P2P networks: eDonkey, Gnutella, JXTA
Jobs sites: LinkedIn, Ryze, ...
Friendster, Tribes, Orkut and other “social” sites.
Opensource Developers communities: Advogato.org (Affero.org)
Hospitalityclub.org, couchsurfing.com: hosting in your house unknown people?
Bookcrossing and lending stuff sites.
Network of personal weblogs (the blogroll is your trust list)
Semantic Web: FOAF (FriendOfAFriend) is an RDF format that allows to express social
relationships (~10 millions files) and XFN microformat
PageRank (Google) ... MyWeb2.0 (Yahoo!)
7. Trust networks (are graphs)
Aggregate all the trust statements to produce a
trust network. A node is a user (example: Dr.P).
A direct edge is a trust statement
0
Mena Ben In Epinions,
0.2 Properties of Trust: just 1 and 0!
0.9 weighted (0=distrust, 1=max trust)
0.6
subjective
1
Dr.P Doc asymmetric contextdependent
Trust Metric (TM):
? Uses existing edges for predicting values
of trust for nonexisting edges,
thanks to trust propagation (if you trust
someone, then you have some degree of
trust in anyone that person trusts).
8. TM perspective: Local or Global
1 1
Mary Mena Bill
How much Bill can be trusted?
0 On average (by the community)?
By Mary?
ME 1
Doc And by ME?
Global Trust Metrics:
“Reputation” of user is based on number and quality of incoming edges. Bill has
just one predicted trust value (0.5).
PageRank (Google), eBay, Slashdot, ... Works badly for controversial people
Local Trust Metrics
Trust is subjective > consider personal views (trust “Bill”?)
Local can be more effective if people are not standardized.
9. Controversial Users
Intuitively: a Controversial User is
TRUSTED by some users
DISTRUSTED by some users
Do you want an example?
10. Controversial Users: an example
1 0
1 0
1 0
1 0
1 0
(....) (....)
1 0
100M people 100M people
If you don't know Bush, should you trust Bush?
T(Bush)=0.5? Make sense? Here global metrics don't.
11. Controversial Users: an example
1 0
1 0
1 0
1
1 1 0
R 1 0
D
1 1
(....) (....)
1 0
100M people 100Mpeople
Local Metric makes more sense. Your trust in Bush
depends on your trusted users!
T(R,Bush)=1 T(D,Bush)=0
12. Controversial Users on Epinions
Controversial users are normal in societies
How many controversial users on Epinions.com?
But first, two definitions of Controversiality:
Controversiality Level of A: number of users that
disagree with the majority = Min(#trust, #distrust)
Contr. “Percentage” of A = (TD) / (T+D) in [1, 1]
CP(A)=1 if A is trusted by everyone (loved!)
CP(A)=1 if A is distrusted by everyone (hated!)
CP(A)=0 if A is trusted by n users and distrusted by n users
13. Experiment
Epinions.com dataset
Real Users: ~150K
Edges (Trust / Distrust): 841K (717K / 124K)
~85K received at least one judgement (trust or distrust)
17.090 (>20%) are at least 1controversial (at least 1 user
disagrees with the majority) > Non negligible portion!
1.247 are at least 10controversial
144 are at least 40controversial
1 user is 212controversial! (~400 trust her, 212 distrust her)
14. Experiment
Comparing 2 metrics about accuracy in trust/distrust
prediction.
Global: ebaylike. Trust(A)=#trust/(#trust+#distrust)
Local: MoleTrust, based on Trust Propagation from current
user (simple and fast)
Cycles are a problem > Order peers
based on distance from source user
Trust of users at level k is based only
on trust of users at level k1 (and k)
Trust propagation horizon & decay
15. Experiment
How do we compare metrics?
Leaveoneout: Remove an edge in Trust Network and
try to predict it. Then compute error as absolute
difference between Real and Predicted value.
Also differentiating over trust or distrust statements
16. Exp. on Controversiality Level
y=error made by TM predicting
edges on users with x
Error
controversiality level.
Predicting Distrust is more
Ebay Controversiality level
difficult.
Ebay error on Distrust ~ 0.6
Mole2 error on Distrust ~ 0.4
Error
Error on Trust is similar because
(#trust >> #distrust)
MoleTrust2 Controversiality level
17. Exp. on Controversiality Percentage
CP~0 = Controversial User
Error Ebay = 0.5 on Contr.Us
Error
Error MoleTrust2 smaller
but not as small as we
Ebay Controversiality percentage would like: can we reach 0?
Other experiments in paper:
Error on Trust Edges.
Error
Error on Distrust Edges (very
important to correctly predict
these ones!)
MoleTrust2 Controversiality percentage
18. Other experiments
MoleTrust with different propagation horizons
2, 3, 4
Computing Coverage.
19. Conclusions
In complex societies, it is normal that someone likes
you and someone dislikes you.
Most Papers make assumption of unique quality
value for a peer (and propose an algo for predicting it)
This is IRREALISTIC! (I know this is intuitive but
still ...)
20. Conclusions (2)
As a consequence, we need Local Trust Metrics.
Local TMs are computationally much more expensive than
Global TMs! > Possibly, you run it locally for yourself on
your mobile or on your browser (should be fast!)
Local TMs exploits less information > reduced coverage.
Global Metrics fine in noncontroversial domains: possibly
ok on Ebay, surely not ok on sites about (political?) opinions
Trust networks are everywhere!
More research is needed: Yahoo! (with MyWeb2.0)
and Google are there. More real testbeds, more
proposals of Local TMs, more comparisons, ...
21. Licence of this slides
These slides are released under
Creative Commons
AttributionShareAlike 2.5
You are free:
* to copy, distribute, display, and perform the work
* to make derivative works
* to make commercial use of the work
Under the following conditions:
Attribution. You must attribute the work in the manner specified by the author or licensor.
Share Alike. If you alter, transform, or build upon this work, you may distribute the resulting work only under a license identical to
this one.
* For any reuse or distribution, you must make clear to others the license terms of this work.
* Any of these conditions can be waived if you get permission from the copyright holder.
Your fair use and other rights are in no way affected by the above.
More info at http://creativecommons.org/licenses/bysa/2.5/
22. The End
The End.
Thanks for your attention!
Questions?