Political Discussions in Homogeneous and Cross-Cutting Communication Spaces

1. Political Discussions in
Homogeneous and Cross-Cutting
Communication Spaces
Jisun An, Haewoon Kwak, Oliver Posegga, Andreas Jungherr

2. Image Source: https://www.youmethoughts.com/participating-and-leading-a-group-discussion/
Online communication spaces to express political opinions,
share information, or exchange contrary political views.

3. Homogeneous
discussion spaces
Cross-cutting
discussion spaces

4. Debate on the effects of the structure of political
talk online on democracy
Cross-cutting
spaces
Homogeneous
spaces
Beneficial Exchanges across
partisan political lines are
beneficial for healthy
democracies
Fostering political
participation

5. Debate on the effects of the structure of political
talk online on democracy
Cross-cutting
spaces
Homogeneous
spaces
Beneficial Exchanges across
partisan political lines are
beneficial for healthy
democracies
Fostering political
participation
Harmful The deterioration of
political discourse
Becoming less likely to
trust important decisions
to people whose values
differ from their own

6. Homogeneous Discussion Cross-cutting Discussion
Many studies on general conditions or
structures of political discussion exist, but
we know little about the actual behavior of
users in politically homogeneous or
cross-cutting communication spaces.

7. Research Questions
[User Activities and Interactions] RQ1: To what degree
do users with different political leaning interact with each
other in politically cross-cutting environments?
[Linguistic Patterns] RQ2: Is there evidence that these
users shift the linguistic style and word use of their posts
between interactions in homogeneous versus
cross-cutting environments?

8. Where can we find both homogeneous
and cross-cutting discussion spaces
online?

10. Reddit: the front page of the internet
As of March 2019, Reddit had
- 542 million monthly visitors (234 million unique users)
- Ranking as the No. 6 most visited website in U.S. according
to Alexa Internet

11. Subreddits:
a forum dedicated
to a specific topic
on the website
Reddit

12. Hierarchical
Commenting
Interfaces:
Forming
discussion
trees

13. Scoring
system:
users can
score both
posts and
comments

14. Political exchanges in Reddit
Best platform to study the nature of political exchanges in
homogeneous and cross-cutting communication spaces.
Cross-cutting spaces
/r/politics
/r/news
Homogeneous spaces
/r/hillaryclinton
/r/The_Donald

15. Dataset
We collect all posts and comments posted to the four subreddits
from 2016/01/01 to 2016/12/31 from
homogeneous
cross-cutting
In total: 2.5M posts and 39.8M comments

16. Users who are active (posted more than ten comments) and having
positive average score of their comments in homogeneous spaces.
Clinton Supporters (SC
): 6,021
Trump Supporters (ST
): 66,074
Unassigned users (SU
): 265,663
Definition of Clinton/Trump “Supporters”
Image Source: https://www.trulia.com/research/election-survey/

17. Interaction Type Classification
“Interaction”: direct replies to posts or comments of users

18. User Activity and
Interactions
Linguistic Patterns:
Styles, Wordings, and
Semantics
?
Image Source: https://www.vox.com/policy-and-politics/2016/11/9/13570724/media-obsession-emails

19. User Activity and
Interactions
Linguistic Patterns:
Styles, Wordings, and
Semantics
?

20. Are Clinton/Trump supporters active in both
type of spaces?
Clinton
Supporters
(6,021)
Trump
Supporter
(66,074)
/r/hillaryclinton 165 comments on avg.
/r/The_Donald 144 comments on avg.
/r/politics
78% users
299 comments on avg.
87K posts
1.4M comments
61% users
84 comments on avg.
141K posts
3.3M comments
/r/news
49% users
31 comments on avg.
5.8K posts
90K comments
49% users
28 comments on avg.
32K posts
902K comments

21. Are Clinton/Trump supporters active in both
type of spaces?
Clinton
Supporters
(6,021)
Trump
Supporter
(66,074)
/r/hillaryclinton 165 comments on avg.
/r/The_Donald 144 comments on avg.
/r/politics
78% users
299 comments on avg.
87K posts
1.4M comments
61% users
84 comments on avg.
141K posts
3.3M comments
/r/news
49% users
31 comments on avg.
5.8K posts
90K comments
49% users
28 comments on avg.
32K posts
902K comments

22. Are Clinton/Trump supporters active in both
type of spaces?
Clinton
Supporters
(6,021)
Trump
Supporter
(66,074)
/r/hillaryclinton 165 comments on avg.
/r/The_Donald 144 comments on avg.
/r/politics
78% users
299 comments on avg.
87K posts
1.4M comments
61% users
84 comments on avg.
141K posts
3.3M comments
/r/news
49% users
31 comments on avg.
5.8K posts
90K comments
49% users
28 comments on avg.
32K posts
902K comments

23. Are Clinton/Trump supporters active in both
type of spaces?
Clinton
Supporters
(6,021)
Trump
Supporter
(66,074)
/r/hillaryclinton 165 comments on avg.
/r/The_Donald 144 comments on avg.
/r/politics
78% users
299 comments on avg.
87K posts
1.4M comments
61% users
84 comments on avg.
141K posts
3.3M comments
/r/news
49% users
31 comments on avg.
5.8K posts
90K comments
49% users
28 comments on avg.
32K posts
902K comments
#1: Both supporters expose themselves to
cross-cutting political spaces, but Trump
supporters do much less actively than Clinton
supporters.

24. Do Clinton/Trump supporters interact to each
other in cross-cutting discussion spaces?

25. Do Clinton/Trump supporters interact to each
other in cross-cutting discussion spaces?
Homogeneous
discussion spaces
Cross-cutting
discussion spaces

26. Do Clinton/Trump supporters interact to each
other in cross-cutting discussion spaces?
Homogeneous
discussion spaces
Cross-cutting
discussion spaces

27. We build a null model
to estimate the
expected frequency of
each interaction type
Network node-type shuffling
Keep the network structure
the same but shuffle the user type
randomly

28. To measure whether supporters of opposing
candidates interacted more frequently than
random chance would suggest
Higher z-score → “being observed more often than chance”
Observed
Expected
Expected

29. Do Clinton/Trump supporters interact to each
other in cross-cutting discussion spaces?
Homogeneous
discussion spaces
Cross-cutting
discussion spaces
Interaction associated with Trump and Clinton supporters have all
positive z-score, meaning they are empirically observed more
often than in the random model.

30. Do Clinton/Trump supporters interact to each
other in cross-cutting discussion spaces?
Homogeneous
discussion spaces
Cross-cutting
discussion spaces
#2: Clinton and Trump supporters not only
participate in parallel but actually do interact
across party lines in cross-cutting spaces.

31. User Activity and
Interactions
Linguistic Patterns:
Styles, Wordings, and
Semantics
?

32. Linguistic Inquiry and Word
Count (LIWC)
93 categories including parts of
speech, topical and
psychological categories, and
emotions
Widely used in the analysis of
political communication
To study linguistic styles
A subset of the LIWC 2015 categories
Image source: https://www.semanticscholar.org/paper/Gender-Classification-with-Data-Independent-in-Isbister-Kaati/f9e37b824957d44bea0ae2507ca73ba75f32ed1d

33. among supporters of the same candidate when interacting
in homogeneous vs cross-cutting communication spaces
by conducting the paired t-test for each of 93 LIWC categories with
Bonferroni correction for multiple comparisons.
Linguistic styles shift
vs

34. LIWC categories that are consistently used
more often either in homogeneous or
cross-cutting spaces

35. In homogeneous discussion spaces
Supporters appear more open, happy, and comfortable

36. In homogeneous discussion spaces
Supporters express greater group responsibility by using more
third-person plural pronouns (“we”)

37. In homogeneous discussion spaces
Supporters use more affiliation and reward words, indicating that
they talk more about their determination and ambition

38. In cross-cutting discussion spaces
A greater use of second-person pronouns (“you”)

39. In cross-cutting discussion spaces
A greater use of words pointing to cognitive processing, causal
reasoning, and negations indicates that the supporters use more
complex language and sophisticated reasoning

40. In cross-cutting discussion spaces
Also, a higher use of question marks implies that questions are
more common in political contributions to cross-cutting spaces.

41. Do supporters change linguistic style when talking
in homogeneous vs cross-cutting spaces?
#3: In homogeneous environments, supporters
feel more open and comfortable and exhibit
greater group responsibility. In cross-cutting
spaces, supporters use more complex language
and sophisticated reasoning.

42. Wordings and semantic differences in
cross-cutting discussion spaces

43. Similarity of vocabulary
For each interaction type (e.g., SC
→ SC,
SC
→ ST
), we rank words
by their summed TF-IDF values across all comments.
We compare the Top-K words across the interaction types by
computing Jaccard Similarity.

44. Similarity of vocabulary (/r/politics)

45. Similarity of vocabulary (/r/politics):
Like-minded vs supporters of opposing candidate
[Clinton Supporters] SC
→ SC
vs. SC
→ ST
: 80.2% on average
[Trump Supporters] ST
→ ST
vs. ST
→ SC
: 88.9% on average
80.2%
88.9%

46. Similarity of vocabulary (/r/politics):
Like-minded vs supporters of opposing candidate
The overall similarities of word use are striking!
80.2%
88.9%

47. The important words might be
overlapped largely.
But, the semantic context of those words
might be different.

48. Examining the top 20 context words for
“women” for each interaction type
SC
→ SC
Feminism (e.g., feminists, feminism)
and minority issues (e.g., gays, minorities)
SC
→ ST
Feminism (e.g., feminists, genders) and sexual
harassment (e.g., groping, assaults, unwanted, restroom)
ST
→ ST
Fantasizing about women
(e.g., fantasized, fantasizing, fantasizes)
ST
→ SC
Homosexuality (e.g., lesbians, homosexually)
and sexual harassment (e.g.,accusers, harassed)

49. Examining the top 20 context words for
“women” for each interaction type
SC
→ SC
Feminism (e.g., feminists, feminism)
and minority issues (e.g., gays, minorities)
SC
→ ST
Feminism (e.g., feminists, genders) and sexual
harassment (e.g., groping, assaults, unwanted, restroom)
ST
→ ST
Fantasizing about women
(e.g., fantasized, fantasizing, fantasizes)
ST
→ SC
Homosexuality (e.g., lesbians, homosexually)
and sexual harassment (e.g.,accusers, harassed)

50. Examining the top 20 context words for
“women” for each interaction type
SC
→ SC
Feminism (e.g., feminists, feminism)
and minority issues (e.g., gays, minorities)
SC
→ ST
Feminism (e.g., feminists, genders) and sexual
harassment (e.g., groping, assaults, unwanted, restroom)
ST
→ ST
Fantasizing about women
(e.g., fantasized, fantasizing, fantasizes)
ST
→ SC
Homosexuality (e.g., lesbians, homosexually)
and sexual harassment (e.g.,accusers, harassed)

51. Examining the top 20 context words for
“women” for each interaction type
SC
→ SC
Feminism (e.g., feminists, feminism)
and minority issues (e.g., gays, minorities)
SC
→ ST
Feminism (e.g., feminists, genders) and sexual
harassment (e.g., groping, assaults, unwanted, restroom)
ST
→ ST
Fantasizing about women
(e.g., fantasized, fantasizing, fantasizes)
ST
→ SC
Homosexuality (e.g., lesbians, homosexually)
and sexual harassment (e.g.,accusers, harassed)

52. Examining the top 20 context words for
“women” for each interaction type
SC
→ SC
Feminism (e.g., feminists, feminism)
and minority issues (e.g., gays, minorities)
SC
→ ST
Feminism (e.g., feminists, genders) and sexual
harassment (e.g., groping, assaults, unwanted, restroom)
ST
→ ST
Fantasizing about women
(e.g., fantasized, fantasizing, fantasizes)
ST
→ SC
Homosexuality (e.g., lesbians, homosexually)
and sexual harassment (e.g.,accusers, harassed)
Can we quantify semantic difference of
“Women”, for example, in SC
→ SC
vs SC
→ ST
?

53. 1. Training word embeddings using skip-gram model for each
corpus of each interaction type (e.g., SC
→ ST
)
2. Aligning embeddings to ensure that the two word embeddings
use the same coordinate axes
3. Quantifying semantic differences by computing the cosine
distance of word’s vector representations between different
word embeddings
Quantify semantic differences in the context of
words used

54. Among Clinton supporters
“Clinton will win the race”
“Clinton is the best”
“Trump will lose the race”
“Trump is a liar”
Among Trump supporters
“Trump will win the race”
“Trump is the best”
“Clinton will lose the race”
“Clinton is a liar”
Two toy corpora

55. TrumpClinton
Lose
Liar
Win
Trump Clinton
Best
Lose
Liar
Win
Train word embedding on each corpus
Among Clinton supporters
“Clinton will win the race”
“Clinton is the best”
“Trump will lose the race”
“Trump is a liar”
Among Trump supporters
“Trump will win the race”
“Trump is the best”
“Clinton will lose the race”
“Clinton is a liar”
Best

56. TrumpClinton
Lose
Liar
Win
Trump Clinton
Best
Lose
Liar
Win
Train word embedding on each corpus
Among Clinton supporters
“Clinton will win the race”
“Clinton is the best”
“Trump will lose the race”
“Trump is a liar”
Among Trump supporters
“Trump will win the race”
“Trump is the best”
“Clinton will lose the race”
“Clinton is a liar”
Best
Semantic difference on “Clinton” between the two corpora?

57. TrumpClinton
Lose
Liar
Win
Trump Clinton
Best
Lose
Liar
Win
Train word embedding on each corpus
Among Clinton supporters
“Clinton will win the race”
“Clinton is the best”
“Trump will lose the race”
“Trump is a liar”
Among Trump supporters
“Trump will win the race”
“Trump is the best”
“Clinton will lose the race”
“Clinton is a liar”
Best

58. TrumpClinton
Best
Lose
Liar
Win
Trump Clinton
Best
Lose
Liar
Win
Two word embeddings, aligned
Among Clinton supporters
“Clinton will win the race”
“Clinton is the best”
“Trump will lose the race”
“Trump is a liar”
Among Trump supporters
“Trump will win the race”
“Trump is the best”
“Clinton will lose the race”
“Clinton is a liar”

59. TrumpClinton
Best
Lose
Liar
Win
Trump Clinton
Best
Lose
Liar
Win
Two word vectors from two word embeddings
Among Clinton supporters
“Clinton will win the race”
“Clinton is the best”
“Trump will lose the race”
“Trump is a liar”
Among Trump supporters
“Trump will win the race”
“Trump is the best”
“Clinton will lose the race”
“Clinton is a liar”

60. Clinton ClintonAmong
Clinton
supporters
Among
Trump
supporters
Cosine Distance (cosDist) = 1 - Cosine Similarity
Quantify semantic differences of a word in the two
corpora by cosine distance

61. Examining the top 20 context words for
“women” for each interaction type
SC
→ SC
Feminism
and minority issues
SC
→ ST
Feminism and
sexual harassment
ST
→ ST
Fantasizing about
women
ST
→ SC
Homosexuality and
sexual harassment
cosDist(“women”) = 0.35
cosDist(“women”) = 0.59

62. cosDist(“women”) = 0.24
cosDist(“women”) = 0.16
Examining the top 20 context words for
“women” for each interaction type
cosDist(“women”) = 0.35
cosDist(“women”) = 0.59

63. Word semantics difference by interaction type
Clinton supporters use words in almost the same context when
they are interacting with other Clinton supporters and when they are
interacting with unassigned users (Blue) or Trump supporters (Orange).

64. Word semantics difference by interaction type
Compared to talking to unassigned user (Green), Trump
supporters tend to change the semantic context more when
talking to Clinton supporters (Red).

65. Top words with the highest semantic gap in
supporters of the same vs opposing candidate
War
Women
Job
Tax
Law
Healthcare
America
Debate
Government
Fact
History

66. Do users change word semantics when interacting
with supporters of opposing candidate?
#4: While supporters of different candidates use
similar important words in interactions, we find
the semantic context of these words is varying
clearly by different interaction types.

67. Summary
We introduce a methodological framework to compare interaction
and linguistic patterns of different groups.
We study how users behave in homogeneous or cross-cutting
discussion spaces:

68. #2: Clinton and Trump supporters not only participate in
parallel but actually do interact across party lines in
cross-cutting spaces.
#1: Both supporters expose themselves to cross-cutting
political spaces, but Trump supporters do much less actively
than Clinton supporters.
#4: While supporters of different candidates use similar
important words in interactions, we find the semantic context
of these words is varying clearly by different interaction types.
#3: In homogeneous environments, supporters feel more
open and comfortable and exhibit greater group
responsibility. In cross-cutting spaces, supporters use more
complex language and sophisticated reasoning.

69. Thanks!
jisun.an@acm.org
@jisunan
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