Rounded Rectangles

Studying online conversations in the Korean blogosphere: A network approach

Anatoliy Gruzd (gruzd@dal.ca)Dalhousie University, Canada

Chung Joo Chung (cjchung@buffalo.edu)State University of New York at Buffalo, USA

Han Woo PARK (hanpark@ynu.ac.kr)YeungNam University, Korea

International Sunbelt Social Network Conference
Riva del Garda, Italy July 3, 2010This work is supported by SSHRC on the Canadian side and by the World University project on the Korean side.

What content-based features of online interactions help to uncover nodes and ties between online participants?

Automated Discovery of Social Networks
among Blog Readers/Commentators

?

ICTA - Online Tool for Social Network Discovery
http://TextAnalytics.net

Objectives

Automated analysis of online interactions and conversations in the Korean blogosphere

Develop and evaluate an e-research tool called the Korean Internet Network Miner (KINM)

Dataset

OhMyNews popular blogging website in Korea

Single blog authored by (bangzza) http://blog.ohmynews.com/bangzza

~1,000 comments (April 2008 - April 2009)

.

Sample blog post and comments

Automated Discovery of Social Networks
Chain (reply-to) method

Visualized by CMU ORA

Automated Discovery of Social Networks
Name Network Approach

Method

Connect the sender to people mentioned in the message

Connect people whose names co-occur in the same message(s)

Discovered Tie(s)

Ann -> Steve Ann -> Natasha

Steve Natasha

FROM: AnnSteve and Natasha, I couldn't wait to see your site. I knew it was going to [be] awesome!

This approach looks for personal names in the content of the comments to identify social connections between online participants.

Main Communicative Functions of Personal Names (Leech, 1999)

getting attention and identifying addressee

maintaining and reinforcing social relationships

Names are one of the few textual carriers of identity in discussions on the web (Doherty, 2004)

Their use is crucial for the creation and maintenance of a sense of community (Ubon, 2005)

Automated Discovery of Social Networks
Name Network Approach

Network representation of blog comments

1 (I) 2 (Photographer) 3 (and, so)4 (Tetz)5 (Bangzza)6 (comment)7 (Nokdu)8 (, : ))9 (a little, a bit)10 (people)

Among 10 nodes, only 2, 4, 5 and 7 are NANEs or IDs of participants in the Bangzza blog

1

2

3

5

6

7

10

8

9

4

Semi-automated network evaluation

Clues suggesting that a word is likely to be a nickname

context words such as "" = an honorific or "" = Mr./Ms

full name, which is almost always three characters

punctuation indicative of someone being addressed (e.g., / or :)

combination of characters (Korean, English and/or Chinese), symbols (e.g., underscores, hyphens) and numbers

patterns indicative of non-native wordsphonetic koreanization of English (e.g., "" = mediamogul = Media Mogul)

phonetic romanization of Korean (e.g. jihwaja = )

Words that are NOT likely to be used as a nickname

a word candidate is a phrase e.g., if the FROM field is used more like a subject line (possible indicators include white spaces and length)

a word candidate consists of a single character (e.g., a or )

a word candidate consists of netspeakemoticons (e.g. =_=)

slang and abbreviations (e.g., using 2MB to refer to the former Korean president)

onomatopoeia (e.g., " = heehee, " = haha)

Words that are NOT likely to be used as a nickname (2)

a word candidate appears more than one time in the comment

a word candidate consists of random characters (e.g. "" or asdf)

a word candidate is a short, conversational word or phrase (e.g., "" = me,"" = oh no, "" = so/therefore)

a word candidate is a common word or idea in the given context/topic (e.g "" = Republic of Korea, "" = a newly created word used to refer to political fanatics)

Conclusion

A network representation of comments posted to a blog makes it much easier to analyze social interactions among online participants

Even in a blog dominated by mostly anonymous and argumentative commentators, a community can still emerge

Suggested future improvements to our network discovery algorithm.

Acknowledgments

Jaeeun Yoo at the University of Toronto for her help with the data analysis

The project is in part supported by