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Transcript of Chung-Hong Chan and King-Wa Fu: Differential opinions among Hong Kong online social media,...
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Differential opinions among Hong Kong online social media, traditional news media, opinion polls and the government consultations on methods for selecting chief executive in 2017
Communication among Hong Kong Facebook Pages during the #umbrellarevolution
Chung-hong Chan, King-wa FuJournalism and Media Studies Centre, HKUCeDEM-Asia 2014 @chainsawriot
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Credit: NOW TV, RFA, news.com.au, Forbes
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Traditional media
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Social Media / Online Media
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What we know #1
Online Media
Social Media
18-29yTert. Ed
30-49yS3-S7
>60y<S3
Source: CUHK COMvia Ming Pao
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Fu KW, Chan CH. Analyzing Online Sentiment to Predict Opinion Poll Results. Cyberpsychol Behav Soc Netw. 2013 Sep;16(9):702-7
What we know #2
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Conover MD et al.Political Polarization on Twitter.ICWSM 2011
What we know #3
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What we don’t know
1. Polarization of online social media in Hong Kong
2. How this polarization affects the relationship between online sentiment and public opinion in Hong Kong?
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Data source
● Facebook pages about HK(n=885)● Snowball sampling (still growing!)● Publicly available posts from included pages ● from 2014-07-01 (day 1 of the research) to
2014-11-10
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Facebook sharing network
weighted: number of sharing
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Why sharing network?
● Castells (1996, 2009): ‘Network society’● [Communication] Power-making in a network
‘Switchers’: who exercise the ability to connect and coordinate different actors in the network by sharing common interests, values, goals, resources, or cultural materials
● Farrells & Drezner (2008): Bottom-up agenda setting
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Two‐Step Flow Model of Influence Network model of influence
Watts D, Dodds PS. Influentials, Networks, and Public Opinion Formation. J Consumer Res. 2007 34
Network and public opinion formation
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Sharing network
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Betweenness
Betweenness:how often a node is located on the shortest path between nodes in the network
Interpretation:● ‘Facilitator’ of
communication within the network
● Influence over what flow and does not in the network
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Nodes with >95pct betweenness(Node size: betweeness)
Subgraph
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1-Inmedia
2-Kengo Ip健吾3-Scholarism
4-Apple Daily
5-HKFS
Subgraph
Nodes with >95pct betweenness(Node size: betweeness)
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Polarization
Conover MD et al.Political Polarization on Twitter.ICWSM 2011
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Walktrap community detection algo.
● “Clustering”● Random walker tends to be trapped in the
densely connected parts (a.k.a. hidden community) within the network
Pon P, Latapy M.Computing communities in Large Networks Using Random Walk.ISCIS 2005
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Hidden communities found
Six large (> 10 nodes) hidden communities found1. Giant component (364 nodes)2. Activists (208 nodes)3. ProBJ #1 (16 nodes)4. Autonomists (87 nodes)5. ProBJ #2 (27 nodes)6. Trivia (17 nodes)
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Community 1: Giant component
Size: 364 nodes
Node with top3 betweenness: 健吾 (Kengo Ip)學民思潮 (Scholarism)蘋果日報 (Apple Daily)
Keywords:
app, itunes, play, download, google, 真話, egyiyl, 憐憫, memehkdotcom, me
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Community 2: Activists
Size: 208 nodes
Node with top3 betweenness: 獨立媒體 (Inmedia)學聯 (HKFS)和平佔中 (OLPC)
Keywords:
即時, hkclassboycott, 現場, occupycentral, 我們, hongkong, 澳門, umbrellamovement, 採訪, 詳看
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Community 3: Probeijing 1
Size: 16 nodes
Node with top3 betweenness: 我哋係中國香港人 (we are Chinese in Hong Kong)幫港出聲(Silent Majority)香港培青社 (HK Youth Development Society)
Keywords:
天有, admin, 管理員, 豫行, 白少, 簽名, 親眼看到, wechat, 中國, 圖像
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Community 4: Autonomists
Size: 87 nodes
Node with top3 betweenness: 熱血時報(Passion Times)有種美德叫有種 (a blogger)無待堂 (a blogger)
Keywords:
熱血, 制憲, 課金, 時報, 文青, 育成, 抗共, 同步, 打倒, 主持
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Community 5: Probeijing 2
Size: 27 nodes
Node with top3 betweenness: 時聞香港(Hong Kong Good News)向香港警察致敬 (Salute to Hong Kong Police)理性撐國民教育 (Rationally support National Education)
Keywords:
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Community 6: Trivia
Size: 17 nodes
Node with top3 betweenness: 我係香港人(We are Hong Konger)香港地笑話(Hong Kong Jokes)投考公務員資訊 (Information for Civil Service Recruitment)
Keywords:
詳情, 面試, 招聘, 開課, graceyard, edu, 二級, 開班, 筆試, 主任
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Overall FB volume and predictionof CY’s rating using different lag units in day.
(after cancel out the effect of autocorrelation and seasonality usingARIMA (1,1,1)(1,0,0)7 model)
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Frontline: ‘Activists’ pages
-0.136 (95% CI: -0.27 to -0.01)
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Tracking “public opinion” using Facebook volume
“Giant”, “Activists”, “Probeijing2”
“Activists” pages activities are correlated with CY Leung’s rating in the largest lag unit. (18 days)
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Preliminary: “Sentiment towards gov”
A sentiment model was trained with only 400 human-coded facebook posts (300 training, 100 validation)
Test set F1 score: 75%
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Useful features associated with negative sentiment
hkclassboycott/umbrellarevolution/主席/人士/人大/人數/他們/佔中/佔領/全球/公民/原則/反佔中/呼籲/和平/基本法/報告/如何/學生/學聯/對話/市民/很多/感到/批評/抗爭/支持/政府/政改/政治/日本/旺角/普選/服務/未有/梁振英/民主/民意/沒有/清場/港人/爭取/特首/現場/現時/發起人/示威/示威者/社會/社民連/立法會/簽名/繼續/罷課/行動/表示/表達/認同/認為/警察/警方/議員/進行/遊行/運動/選舉/邀請/重新/金鐘/集會/難以/雨傘/革命/香港/黃之鋒
More like a political content filter
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Ongoing
● Long term temporal relationship● Better sentiment filter
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http://weiboscope.jmsc.hku.hk/tbj/(Coming soon!)
http://weiboscope.jmsc.hku.hk/
@chainsawriot