1. Analyzing the Political Blogosphere.
19 March 2007

2. 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
1. Dynamic and interactive than the static webpages of web 1.0
11/15/2018

3. Blogs – Essence of Social Media
Blogs spread new ideas and information rapidly
11/15/2018

4. Knowing & Influencing your Audience
Your goal is to campaign for a presidential candidate
How can you track the buzz about him/her?
What are the relevant communities and bogs?
Which communities are supporters, which are skeptical, which are put off by the hype?
Is your campaign having an effect? The desired effect?
Which bloggers are influential with political audience? Of these, which are already onboard and which are lost causes?
To whom should you send details or talk to?
11/15/2018

5. Influence Detection
Often voters are influenced by opinions and reviews on blogs
Detecting influential nodes and their role in how people perceive a political party could be an important tool during campaigning
Using topic, social structure, opinions, biases and temporal information we can develop an accurate model for influence
11/15/2018

6. Influence in Communities
Communities detected using “Fast algorithm for detecting community structure in networks”, M.E. J. Newman
11/15/2018

7. Influence of MSM
Citation count alone is not an indicator of influence; who cites is a factor.
Using a list of 130 dem and 140 rep blogs
11/15/2018

8. Computing Influence of MSM
For Democratic Citations
Score(i) = Pd(i)•log(Pd(i)/Pr(i))•Nd(i)
where
i is the MSM source
Pd(i): probability that a democratic blog links to MSM i
Pr(i): probability that a republican blog links to MSM i
Nd(i): number of distinct democratic blogs linking to i
Similar ranking for republican blogs
11/15/2018

9. Opinions in Social Media
TREC 06: Finding opinionated posts, either positive or negative, about a query
2006 TREC Blog corpus:
80K blogs
300K posts
50 test queries
Challenges: open domain sentiment words, slangs, subject
Reader’s Perspective
“Starbucks Sandwiches are bad!”
“I went to school early so I would have time to grab some lunch. Which ended up consisting of a crappy sandwich from starbucks and a chai latte. Lacey came into Starbucks while I was there so we chatted for a little bit and she thought that I might be in her class. After I finished eating I headed to school and checked the board……..”1
Narrative
Expressed Opinions
Opinions can effect buying decisions of customers
[1]
11/15/2018

10. Finding Feeds That Matter
Analysis of Bloglines Feeds
83K publicly listed subscribers
2.8M feeds, 500K are unique
26K users (35%) use folders to organize subscriptions
Data collected in May 2006
Before Merge
After Merge
11/15/2018

11. Finding Feeds That Matter
Top Feeds for “Politics” (Merging: “political”, “political blogs”)
Talking Points Memo: by Joshua Micah Marshall
Daily Kos: State of the Nation
Eschaton
The Washington Monthly
Wonkette, Politics for People with Dirty Minds
Informed Comment
Power Line
AMERICAblog: Because a great nation deserves the truth
Crooks and Liars
11/15/2018

12. Finding Feeds That Matter
Tag Based Feed Recommender: Feeds under similar folder names
Recommended Feeds
11/15/2018

13. Finding Influential Feeds using “Co-Citations”
Feed recommendations
Blogs influenced by seed set
Leading blogs about “Politics”. Seed set is top blogs in “politics” from bloglines and blog graph used is from Blogpulse dataset..
11/15/2018

14. Link Polarity / Bias Linking alone is not indicator of influence
Polarity can indicate the type of influence
Consistent negative / positive opinion over a period of time can indicate bias
Link polarity/citation signal can also be helpful in determining trust
Strong Negative Opinion
Mildly Negative opinion
Republican Blog
Democrat Blog
11/15/2018

15. Modeling Influence Using Link Polarity
Motivation
Growing interest in exploring role of communities in social media
Better community detection algorithms using sentiment associated with links
Convert sparsely connected blog graph into a densely connected one with sentiment weight attached to every link
Approach
Link Polarity: Analyze post text surrounding links to determine bias of bloggers about each other
Trust Propagation: Use trust propagation models to spread the polarity from a small subset of “connected” bloggers to all bloggers.
Experiments
Study political blogosphere with goal to classify blogs as left/right leaning
Bias detection using positive/neural/negative score from influential bloggers (high in-link blogs) in both communities
Validation with a hand-labeled dataset indicates ~60% correct classification
11/15/2018

16. Bird’s Eye View – Step 1 E C B D
foo F A
11/15/2018

17. Bird’s Eye View – Step 2 E C B “He is great” D foo “What crap!”
“I like him”
“ridiculous” F A
-ve bias
+ve bias
11/15/2018

18. Bird’s Eye View – Step 3 E C B D
foo F A
-ve bias
+ve bias
11/15/2018

19. Bird’s Eye View – Step 4 E C B D
foo F A
-ve bias
+ve bias
11/15/2018

20. Bird’s Eye View – Step 4 E C B D
foo F A
-ve bias
+ve bias
11/15/2018

21. 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 = 0.33
[2]
11/15/2018

22. Trust Propagation
Based on Guha’s work on propagating trust and distrust
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: Proc. 13th Int. World Wide Web Conf., New York, NY, USA, May ACM Press, 2004.
11/15/2018

23. Experiments Domain Reference Dataset Goal Political Blogosphere
Dataset from Buzzmetrics[2] provides post-post links over 1.5M posts
Few off-the-topic posts help aggregation
Potential business value
Reference Dataset
Adamic’s [3] Hand-labeled dataset classifies blogs as right or left leaning
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, MAY Buzzmetrics –
11/15/2018

24. Effect of Link Polarity
Trust propagation on polar links more effective than on non-polar.
Republican blogs classified more correctly
Effect of Link Polarity
Link Polarity yields ~30% classification improvement
Convergence after 20 propagations
11/15/2018

25. Effect of text window size
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
11/15/2018

26. Sample Data
Trust propagation compensates for initial incorrect polarity (DK–AT)
Trust propagation doesn’t change correct polarity (AT-DK)
Trust propagation assigns correct polarity for non-existent links (AT-IP)
Numbers in italics problematic (AT-MM)
Make polarities below threshold zero?
Improve sentiment detection?
11/15/2018

27. Future Work Bias and trustworthiness of MSM sources
Trend extraction and meme tracking for political blogs
Real time classification positive and negative opinions for presidential candidates
Determining genres in political opinions using content analysis.
11/15/2018