Quantifying Collective Mood by Emoticon Networks

Kazutoshi Sasahara Graduate School of Information Science,

Nagoya University

WebSci’14 PK1

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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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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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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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 = pxn+1,xn ,yn

∑ (xn+1, xn, yn )log2p(xn+1 | xn, yn )p(xn+1 | xn )

X,Y : Discretized tweet-count seriesY ': Random shuffling of Y

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X

Information

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Frequency Distribution of Emoticons and Kanji Characters

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100Red: 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

Relative frequency

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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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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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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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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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