This document proposes using emoticon networks to explore collective mood in social media. Emoticon networks visualize information flows between Japanese emoticons and adjectives in tweets. Analysis found that before a 2011 earthquake, emoticon networks showed subsequent chains of positive and negative events, while after the earthquake they showed alternating chains of positive and negative elements. More validation and comparison to other network models is needed.

1. Quantifying Collective Mood
by Emoticon Networks
Kazutoshi Sasahara
Graduate School of Information Science,
Nagoya University
WebSci 14
PK1

2. Collective Mood
n Tweet analysis demonstrated daily and weekly mood swings.
n Similar patterns were also found by Pulse of the Nation
project.
Golder and Macy (2011), Science
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3. Research Objectives
n Collective Mood
linked with real-life events often emerge in social media, the
observations of which may provide insights into human
nature.
n Emoticon Networks
is proposed to explore collective mood in social media. These
networks visualize the nontrivial nature of information flows
between Japanese emoticons and adjectives.
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4. Data Collection
n Tweets (user timelines) were collected by
a snowball sampling using Twitter REST API.
n Dataset
n 400,000 users
n 500,000,000 tweets
n 2010/1 2011/12
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Reply/RT
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5. Recipe for Emoticon Networks
n Emoticon Networks
n Nodes: Japanese emoticons (e.g., ^o^, T_T, ^^;)
and adjectives
n Directed links: Information flows among nodes
→ Effective transfer entropy
n Effective Transfer Entropy
ETY→X = TY→X −TY '→X
TY→X = p
xn+1,xn,yn
∑ (xn+1, xn, yn )log2
p(xn+1 | xn, yn )
p(xn+1 | xn )
X,Y: Discretized tweet-count series
Y ': Random shuffling of Y
Y
X
Information
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6. Frequency Distribution of
Emoticons and Kanji Characters
10
0
10
1
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2
10
310
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10
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10
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-1
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0
Red: Positive Blue: Negative
Rank
Rank Emoticon/Kanji
Relative
frequency
1 （笑）
0.159
2 (^o^) 0.104
3 ^_^ 0.068
4 (^o^)／
0.039
5 ^^; 0.039
6 ( ́ ▽ ` )ﾉ
0.034
7 ＼(^o^)／
0.034
8 ^_^; 0.033
9 (^O^) 0.033
10 orz 0.030
Relativefrequency
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7. Tweet Series Before & After 2011
Japan Earthquake
n Most emoticons drastically decreased except T_T .
n While negative ones increased, positive adjectives decreased.
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8. Emoticon Networks Before & After
2011 Japan Earthquake
^_^;
T_T
やばい
＾o＾
´Д｀
面白い
すごい
楽しい
￣^￣
ひどい
怖い ^_^; ＾o＾
´Д｀
楽しい
ひどい 怖い
すごい
￣^￣
T_T
面白い やばい
Before After
Loop
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9. Summary
n We proposed emoticon networks as a tool for exploring
collective mood in online social media.
n We applied our method to demonstrate the dynamics of
collective mood before and after the 2011 Japan earthquake:
n Before: Subsequent chains of positive (negative) events
n After: Alternating chains of positive and negative elements
Closed loop
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10. Future Works
n Need more analysis …
n Validation
At present, it is difficult to evaluate whether or not the
resulting emoticon networks are appropriate.
n Comparison
It may be meaningful to compare emoticon networks with
co-occurrence networks where nodes denote Japanese
emoticons and adjectives, and when these co-occur in the
same tweets undirected links are attached.
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