Politics_Moscow.pptx

7/30/2019 Politics_Moscow.pptx

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Political Search TrendsWebSci12, SIGIR12 (demo)

Joint Work with E. Borra and K. Garimella


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Political Search Trends

http://politicalsearchtrends.sandbox.yahoo.com/
http://politicalsearchtrends.sandbox.yahoo.com/http://politicalsearchtrends.sandbox.yahoo.com/


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Point of Departure: Labeled BlogsLeft leaning blogs (387) Right leaning blogs (644)

From Benkler and Shaw A tale of two blogospheres(2010) and Wonkosphere Blog Directory


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Who are these People?

Use self-provided age and gender and ZIP-

derived estimates

People clicking on right-leaning blogs:

Are older (50 vs. 45 years)

Are more male (63% vs. 55%)

Are more white (81% vs. 78%)

More likely to work for Yahoo (92.3% vs. 11.4%)

All these trends agree with voters demographics


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huffingtonpost.com is left-leaning a left-leaning vote for pizza is a vegetableAggregate votes across all clicks on political blogs to compute overall leaning

From Blogs to Queries

vL = left-clicks for queryVL = total left clicks


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Examples of Assigned Leaning

Examples using Wikipedia mapping for 6 months of data, July 4, 2011 January 8, 2012.

queries for Wikipedia entity Patient Protection & Affordable Care Actobama healthcare bill text (.91) who pays for obamacare (.04)

obama health care privileges (.83) obamacare reaches the supreme court (.09)

is affordable care act unconstitutional (.78) is obamacare constitutional (.16)

queries for Wikipedia category Occupy

who started occupy wall street (.94) occupy wall street rape (.09)

we are the 99% (.91) occupy movement violence (.25)

occupy movement supporters (.78) crime in occupy movement (.44)

liesprotest
http://politicalsearchtrends.sandbox.yahoo.com/?q=lieshttp://politicalsearchtrends.sandbox.yahoo.com/?q=protesthttp://politicalsearchtrends.sandbox.yahoo.com/?q=protesthttp://politicalsearchtrends.sandbox.yahoo.com/?q=lies


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``cost obama trip to india

Mapping Queries to Statements

364 distinct queries mapped to true facts

574 distinct queries mapped to false facts


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Correlation with leaning? Any guess? None.

Correlation with leaning, when conditioned on

source? Any guess? None.

Correlation with volume? Any guess?

Well ...

Impact of Truth Value


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Political Twitter Trends

Under reviewJoint Work with Venkata Garimella and

Asmelash Teka


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Twitter and Politics


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Hashtag Wars


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Political Twitter Trends (PTT)

Show live demo


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Data Set

Start with seed set of users with known

political orientation, e.g. @BarackObama or

@MittRomney

Get their tweets


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Extending from Seed Set

Get all the retweets


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U.S. Users Only

Lots of international interest in U.S politics

People from all over the world retweet

Use Yahoo! Placemaker to remove non-US users

http://developer.yahoo.com/geo/placemaker/


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Evaluating Data Quality

Do we have the correct political leaning?

Accuracy = 0.98, 0.93 for Wefollow and Twellow respectively

Inspection: greatest environmentalist. Also, despise republicans

Corrected accuracy: 0.99 and 0.95


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From Users to Back to Hashtags

Tag cloud for left users Tag cloud for right users


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Detecting Political Hashtags

Most hashtags are non-political #fb, #FavouriteAlbums,

Not always obvious

#yes4m, #usmc, co-occurrence with seed political hashtags: #p2,

#tcot,#gop, #ows, obama*, romney*,

Keep top 10% in terms of P(POL|h)

Time dependence #america during Olympics and during elections

Remove low volume hashtags

Mostly noise and no large political issues


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Detecting Trending Hashtags

Trending = currently popular

Having a higher volume than expected

#obamagotosama: May 1, to May 8, 2011

#ows: Sep. 25, to Oct. 2, 2011

Non-trending hashtags: #vote, #democracy


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Assigning a Leaning to Hashtags

Voting approach:

Mere counts:

Normalized counts:

+ smoothing:


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Leanings over Time: Constant


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Leanings over Time: Shifting


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Leanings over Time: Outliers


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Detecting Change Points

Filter hashtags without sufficient support

Total number of weeks > 4

Relat. and absol. change in leaning from previous week

Change from previous week > std and

Change from previous week > 0.25

Change from average value is big

Current value - Average value > std

Change in leaning is in the direction of other leaning

Change in direction = TRUE


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Detected Change Points


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What Causes Change Points

Volume-to-user ratio:

High means small, active set (hijackers)

Low means general masses


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Description of Hijackers


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Topical Clustering of Hashtags

Hashtags are often micro-topics

Cluster hashtags to have more high-level topics

We used simple k-means clustering on co-

occurrence feature vectors


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Cluster Evolution Over Time


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Ongoing Work

Beyond 2-party systems: UK and Germany

Fractional party membership

Visualization challenges Hans Roslings bubbles


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Politics_Moscow.pptx

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