1. Modeling Trust and Influence on Blogosphere using Link Polarity
Anubhav Kale
Masters Thesis, 2007

2. Overview Motivation Problem Statement Approach Link Polarity
Trust Propagation
Experiments
Future Work
Q & A
11/10/2018

3. Overview Motivation Problem Statement Approach Link Polarity
Trust Propagation
Experiments
Future Work
Q & A
11/10/2018

4. Social Media
“Social media describes the online tools and platforms that people use to share opinions, insights, experiences, and perspectives” - wikipedia
Level of user participation and thought sharing across varied topics
Twitterment beta
1. Dynamic and interactive than the static webpages of web 1.0
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5. Blogs – Essence of Social Media
Means by which new ideas and information spreads rapidly on social media
Blog is the only tool that lets people interact heavily with each other
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6. Communities in Blogosphere
Can you track the buzz about Ipod among bloggers ?
What are the blogs that always criticize Ipod and the ones that are Ipod fans ?
Are there any neutral bloggers who would like to have the best of both worlds ?
Can you analyze the changes in opinions/biases ?
Are there any influential blogs in both communities ?
Can you find the right set of individuals (like-minded) to target ?
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7. Overview Motivation Problem Statement Approach Link Polarity
Trust Propagation
Experiments
Future Work
Q & A
11/10/2018

8. Problem Statement
Convert a sparsely connected blog graph without any knowledge of sentiments across blog-blog links, to a densely connected graph with sentiments associated to every link.
Sentiment represents the opinion/trust/distrust of the “blogger” nodes connected by the link.
Use the densely connected “polar” graph to determine like-minded blogs
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9. Overview Motivation Problem Statement Approach Link Polarity
Trust Propagation
Experiments
Future Work
Q & A
11/10/2018

10. Approach
Identify the polarity of link that points from one blog post to another
Simple sentiment detection techniques
Polarity may be positive, negative or neutral
Use trust propagation models to spread the sentiment from the subset of connected blogs to all blogs
Compute polarity from pre-defined influential blogs in each community to deduce like-minded blogs
Validation with a hand-labeled dataset
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11. Bird’s Eye View – Step 1 E C B D
foo F A
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12. Bird’s Eye View – Step 2 “cool!” E C “I like him” B “What crap!”
“He is great” D
foo
“amazing!”
“ridiculous” F A
-ve bias
+ve bias
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13. Bird’s Eye View – Step 3 E C B D
foo F A
-ve bias
+ve bias
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14. Bird’s Eye View – Step 4 E C B D
foo F A
-ve bias
+ve bias
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15. Bird’s Eye View – Step 4 E C B D
foo F A
-ve bias
+ve bias
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16. Overview Motivation Problem Statement Approach Link Polarity
Trust Propagation
Experiments
Future Work
Q & A
11/10/2018

17. Link Polarity Its very generic !
In co-authorship graphs, polarity may be defined as the number of times authors have collaborated
On Amazon.com, polarity is the ranking scheme in the reviews
How does it apply to blogs ?
Represents the opinion of source blog about destination blog
Sign represents whether the bias is for, against or neutral
Magnitude represents the strength or weakness of bias
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18. How to compute polarity ?
Blogrolls
Measure of association between blogs
Indicates that the blogger is interested in following the blog
May not indicate any bias
Static nature – once created, never updated
Blogroll from dailykos 
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19. How to compute polarity ?
Comments
Feedback on complete blog post – granularity is coarse
Verbose comments a challenge for NLP
“Pull” – source blog may not be associated with the comment author
Tendency to comment anonymously on controversial topics
Small piece of text that is used by a random person to interact with bloggers
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20. How to compute polarity ?
Explicit Links
Strongest evidence of interaction
Text surrounding the link generally contains sentiments
Shallow Natural Language Processing can help since the target text is highly focused.
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21. How to compute polarity ?
Explicit Links
Strongest evidence of interaction
Text surrounding the link generally contains sentiments
Shallow Natural Language Processing can help since the target text is highly focused.
Return on investments on links more
11/10/2018

22. Our Approach to Link Polarity
Sentiment Analysis
Calculate the number of positively oriented (Np) and Negatively oriented words (Nn) in the text-window around the link
Apply Stemming, basic canonicalization
Corpus includes simple bi-grams of the form “not_good”
Polarity = (Np – Nn) / (Np + Nn)
Denominator acts as a normalization mechanism
Natural Language Processing is shallow, yet large-scale effects help !
11/10/2018

23. This - http://dailykos.com/storyonly/2006/4/30/1441/59811
Link Polarity Example
“Stephen Colbert's performance at the White House Correspondents' Association dinner has garnered him huge applause in the blogosphere and also on C-Span where it was shown more than once. Those of us who have been angry with Bush for quite some time because of his arrogant and feckless corruption of our country were even more thrilled to see and know that he had no recourse but to sit there and watch his aspirations for greatness be destroyed by a master of irony.
This will be his legacy: I stand by this man. I stand by this man because he stands for things. Not only for things, he stands on things. Things like aircraft carriers and rubble and recently flooded city squares. And that sends a strong message, that no matter what happens to America, she will always rebound -- with the most powerfully staged photo ops in the world. We who have been watching Stephen Colbert eviscerate politicians that have come on his show knew he was a gifted comedian. But it took Saturday's dinner to demonstrate how incredibly effective the art form Colbert has chosen is for exposing the Potemkin Regime Bush and his henchmen have created. Rove and the right wing machine have no answer to the performance but to say "it bombed", "it wasn't funny", and to hope that by ignoring it, the caustic cleansing agent it has lobbed into their camp can be contained. Yet, the Republican spinmeisters are the masters of spin.”[2]
This -
[2]
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24. This - http://dailykos.com/storyonly/2006/4/30/1441/59811
Link Polarity Example
“Stephen Colbert's performance at the White House Correspondents' Association dinner has garnered him huge applause in the blogosphere and also on C-Span where it was shown more than once. Those of us who have been angry with Bush for quite some time because of his arrogant and feckless corruption of our country were even more thrilled to see and know that he had no recourse but to sit there and watch his aspirations for greatness be destroyed by a master of irony.
This will be his legacy: I stand by this man. I stand by this man because he stands for things. Not only for things, he stands on things. Things like aircraft carriers and rubble and recently flooded city squares. And that sends a strong message, that no matter what happens to America, she will always rebound -- with the most powerfully staged photo ops in the world. We who have been watching Stephen Colbert eviscerate politicians that have come on his show knew he was a gifted comedian. But it took Saturday's dinner to demonstrate how incredibly effective the art form Colbert has chosen is for exposing the Potemkin Regime Bush and his henchmen have created. Rove and the right wing machine have no answer to the performance but to say "it bombed", "it wasn't funny", and to hope that by ignoring it, the caustic cleansing agent it has lobbed into their camp can be contained. Yet, the Republican spinmeisters are the masters of spin.”[2]
This -
Np = 8, Nn = 4 ; Polarity = Np – Nn / Np + Nn = 0.33
[2]
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25. Overview Motivation Problem Statement Approach Link Polarity
Trust Propagation
Experiments
Future Work
Q & A
11/10/2018

26. Trust Propagation
Based on work of Guha et al[1] for modeling propagation of trust and distrust
Framework
Mij represents bias from user i to j.(0 <= Mij <= 1)
Belief Matrix M represents the initial set of known beliefs
Mij can be based on trust matrix (T), distrust matrix (D) or a combination of trust and distrust (T-D) from i to j.
T = Positive Polarities and D = Negative Polarities
Goal is to compute all unknown values in M
Results from validations on dataset from “epinions” are impressive
[1] Guha R, Kumar R, Raghavan P, Tomkins A. Propagation of trust and distrust. In: Proceedings of the Thirteenth International World Wide Web Conference, New York, NY, USA, May ACM Press, 2004.
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27. Atomic Propagation Direct Propagation Co-citation
Given: A trusts B and B trusts C
Implies: A trusts C
Operator : M
Co-citation
Given: A trusts B and C, D trust C
Implies: D trusts B
Operator : MT * M B C A A B D C
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28. Atomic Propagation Contd…
Transpose Trust
Given: A trusts B and C trusts B
Implies: C trusts A, A trusts C
Operator : MT
Trust Coupling
Given: D trusts A, A trusts C
and B trusts C
Implies: D trusts B
Operator : M * MT A B C A C D B
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29. Atomic Propagation contd…
Combined Operator
Ci = a1 * M + a2 * MT*M + a3 * MT + a4 * M*MT
ai {0.4, 0.4, 0.1, 0.1} represents weighing factor
Belief Matrix after ith atomic propagation
Mi+1 = Mi * Ci
We perform propagations till “convergence” (till the new iteration does not change values in M above “threshold”)
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30. Models to compute final belief matrix
Trust-only
Ignore distrust (negative polarities) completely
Final Belief Matrix = Mk , M0 = T
(K : Number of atomic propagations till convergence)
One-step Distrust
Distrust propagates single step while trust propagates repeatedly
Final Belief Matrix = Mk * (T-D) , M0 = T
Propagated Distrust
Treat distrust and trust equivalent
Final Belief Matrix = Mk , M0 = T - D
11/10/2018

31. Models to compute final belief matrix
Trust-only
Ignore distrust (negative polarities) completely
Final Belief Matrix = Mk , M0 = T
(K : Number of atomic propagations till convergence)
One-step Distrust
Distrust propagates single step while trust propagates repeatedly
Final Belief Matrix = Mk * (T-D) , M0 = T
Propagated Distrust
Treat distrust and trust equivalent
Final Belief Matrix = Mk , M0 = T - D
11/10/2018

32. Overview Motivation Problem Statement Approach Link Polarity
Trust Propagation
Experiments
Future Work
Q & A
11/10/2018

33. Experiments Domain Reference Dataset Goal Political Blogosphere
Dataset from Buzzmetrics[2] provides post-post link structure over 14 million posts
Few off-the-topic posts help aggregation
Potential business value
Reference Dataset
Hand-labeled dataset from Lada Adamic et al[3] classifying political blogs into right and left leaning bloggers
Timeframe : 2004 presidential elections, over 1500 blogs analyzed
Overlap of 300 blogs between Buzzmetrics and reference dataset
Goal
Classify the blogs in Buzzmetrics dataset as democrat and republic and compare with reference dataset
[2] Lada A. Adamic and Natalie Glance, "The political blogosphere and the 2004 US Election", in Proceedings of the WWW-2005 Workshop
Buzzmetrics –
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34. Effect of Link Polarity
Republican blogs classified more correctly than democrats
Trust propagation on polar links more effective than over non-polar links
Link Polarity improves classification by approximately 26%
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35. Effect of text window size
If we hit another link, we stop
Optimal window size is 750 characters for our experiments
Small window size – Non-opinionated phrases
Large Window size – Analysis of non-related text
Specific to our experiments, numbers may not be generalized
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36. Effect of atomic propagation parameters
X-axis Bitset = {direct trust, co–citation, transpose trust and trust coupling} = { }
Each parameter set to either 0 or its optimal value
Collective influence of all parameters helps !
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37. Evaluation Metrics Confusion Matrix How did I compute the numbers ?
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38. Evaluation Metrics Continued
Accuracy = 73%
True Positive Rate (Recall) = 78%
False Positive Rate (FP) = 31%
True Negative Rate (Recall) = 69%
False Negative Rate (FN) = 21%
Precision (Positive) = 75%
Precision (Negative) = 72%
(Positive – Republican, Negative – Democrat)
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39. Sample Data
Trust propagation compensates for initial incorrect polarity (DK – AT)
Trust propagation does not change correct polarity (AT-DK)
Trust propagation assigns correct polarity for non-existent direct links (AT-IP)
Numbers in italics problematic (MM-AT)
Improve sentiment detection ?
Polarity from you to you is always positive
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40. Main Stream Media Classification
Goal
Classify main stream media news sources (e.g. guardian, foxnews, truthout ) as left and right leaning
Use links from blog posts to media sources ( drop blog-blog links )
Graph Structure P a b
Blogs c Q
MSM d R
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41. MSM Classification Results
Foxnews, guardian, mediamatters
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42. Interesting Observations
24 out of 27 sources classified correctly
Well-known sources like “guardian”, “foxnews”, “truthout” and “mediamatters” classified correctly
Main Outliers -- “thenation” and “boston globe”
“google news” classified as left leaning
Both left and right leaning blogs talk negatively about “nytimes” and “abcnews” and positively about “rawstory” and “examiner”
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43. Overview Motivation Problem Statement Approach Link Polarity
Trust Propagation
Experiments
Future Work
Q & A
11/10/2018

44. Future Work Link Polarity Trust Propagation Experiments
More sophisticated NLP techniques
Topic as a parameter
Trust Propagation
Evaluate other models
Augment trust model with data from other domains (communities in “MySpace” etc)
Experiments
Evaluations on larger heterogeneous datasets
Domains with noisy data and multi-subject posts
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45. Thank You !!
Questions?
11/10/2018