Presentation is loading. Please wait.

Presentation is loading. Please wait.

An Analysis of Political Views on Blogs Todd Sullivan.

Similar presentations

Presentation on theme: "An Analysis of Political Views on Blogs Todd Sullivan."— Presentation transcript:

1 An Analysis of Political Views on Blogs Todd Sullivan

2 General Layout of Project 8 Classes –Candidates: Obama, McCain, Biden, Palin –Parties: Republicans, Democrats, Liberals, Conservatives Extract opinions about each class –Compute a FeelScore metric for each author/class pair (provided the author mentioned the class) Do stuff with the FeelScores

3 Extracting Opinions Define three lists –Class Names –Word Synonyms –Feeling Indicators Tokenize blog posts into sentences Find sentence that contain a class name –Count number of positive/negative Feeling Indicators –Apply counts to classes in sentence

4 Class Names Obama: –obama, senator of illinois, senator from illinois, illinois senator, democrat president, democratic president, … Palin –palin, alaskan governor, governor of alaska, alaska governor, republican vp, … Liberals –liberals, liberal, libs

5 Word Synonyms Bad –worst, unpleasant, disastrous, dirty, failure, sucks, sux, traitor, idiot, loser, noob, dumb, … Good –awesome, sweet, cool, groovy, comforting, glorious, dandy, brilliant, best, wonderful, … Skip Words –Very, usually, still, much, nearly, most, more, frankly, a, an, any kind of punctuation, …

6 Feeling Indicators Negative Indicators –:is: :bad:, :bad: :plans:, :plans: :is: :bad:, :i: do :not: want, :i: cannot stand, how :bad:, :is: :not: :good:, :i: disagree:, :i: do :not:, … Positive Indicators –:is: experienced, :i: agree, :i: will vote, for president, :i: support, :i: donated, :good: speaker, …

7 FeelScore For each author a and class c

8 Incorporating Interests and Communities Pull all unique interests containing one of the class names (obama, mccain, …) –Returns around 350 interests –Small enough to label as positive, negative, or remove. Same process for communities

9 Positive Interests ClassInterest CountAuthor Count Obama33201 McCain514 Biden46 Palin748 Negative Interests ClassInterest CountAuthor Count Obama23 McCain35 Biden00 Palin56

10 Positive Interests ClassInterest CountAuthor Count Republicans25118 Democrats41373 Liberals Conservatives38112 Negative Interests ClassInterest CountAuthor Count Republican1528 Democrat23 Liberal1213 Conservative1017

11 Interesting Interests Positive Interests –Not interesting: obama, mccain 08, republican Negative Interests –_doing_something_ to _class_ where _doing_something_ is: anti, testing, baiting, bashing, hating, pissing off, cockpunching, forced lobotomization of, death to

12 Positive Communities ClassCommunity CountAuthor Count Obama26206 McCain615 Biden13 Palin517 Negative Communities ClassCommunity CountAuthor Count Obama19 McCain213 Biden00 Palin311

13 Positive Communities ClassCommunity CountAuthor Count Republicans826 Democrats19111 Liberals45281 Conservatives917 Negative Communities ClassCommunity CountAuthor Count Republicans00 Democrats00 Liberals310 Conservatives00

14 Example Communities Positive –Obama PA, GothsForObama, TeenRepublicans Negative –Anti-Obama, nobama, WTF-Palin

15 Authors Listing Multiple Interests for the Same Class

16 Authors Joining Multiple Communities for the Same Class

17 FeelScores Across Time For an author a, class c, and day Day

18 Including Interests and Communities Count an interest or community as 5 positive or negative counts added to AdjCount(a,c,Day,...) for all days on and after the interest or community was added to the database.

19 FeelScores Across Time Continued… For a class c, and day Day

20 Overall Blog-based Candidate FeelScores

21 Overall Blog-based Party FeelScores

22 Positive/Negative Candidate Chatter by Blog Network

23 Blog-based Candidate FeelScores Across Networks

24 Blog-based Party FeelScores Across Networks

25 Positive/Negative Chatter Candidate by Gender

26 Blog-based Candidate FeelScores Across Gender

27 Blog-based Party FeelScores Across Gender

28 National Conventions

29 National Conventions New Authors

30 National Conventions Changing Opinions

31 Presidential Debates

32 Presidential Debates Cont…

33 Vice Presidential Debate

34 VP Debate Cont…

35 Obama & McCain FeelScores by Network

36 National Conventions by Network

37 McCain & Palin FeelScores by Network

38 Party FeelScores by Network

39 Aggregate FeelScores by Gender

40 Age Ranges

41 Aggregate FeelScores by Age


43 Older People Dont Like Liberals

44 Friend Networks 523 Democrats and 620 Republicans 1,575 mutual links –64% Democrat-Republican links (Green) –23.6% Democrat-Democrat links (Blue) –12.4% Republican-Republican links (Red) The 64% number is largely influenced by a few authors. In a macro-average across authors, 50.9% of a bloggers friends are from the bloggers party.

45 Percent of Bloggers vs. Number of Friends

46 Political Blogging Friend Network

47 National Convention Poll Data

48 Predicting the Popular Vote No data after October 28, 2008, so we use Oct. 28 data for calculations. For each author a

49 Assigning Votes Continued

50 Popular Vote Classes UsedObamaMcCain # of Authors Obama McCain 56.5%43.4%3,790 Obama-Biden vs. McCain-Palin 53.6%46.4%4,470 Obama-Biden-Democrat vs. McCain-Palin-Republican 51.8%48.2%5,217 Obama-Biden- Democrat-Liberal vs. McCain-Palin- Republican-Conservative 51.2%48.8%5,832 Actual Result: Obama 53%, McCain 46%

51 Exit Polls by Gender CNN Exit Poll Results GenderObamaMcCain# Polled Female56%44%9,453 Male51%49%8,740 Predicted Results GenderObamaMcCain # of Authors Female55.4%44.6%261 Male54.1%45.9%636

52 Exit Polls by Age CNN Exit Poll Results AgeObamaMcCain# Polled 18 to 2967%33%3, to 4453%47%5, to 6450% 6, %54%2,854 Predicted Poll Results AgeObamaMcCain # of Authors 18 to %39.6% to %43.2% to %43.0% %67.7%36

53 Questions?

Download ppt "An Analysis of Political Views on Blogs Todd Sullivan."

Similar presentations

Ads by Google