With the rising popularity of social media such as Facebook, Twitter, Instagram and many more, sentiment classification for social media has become a hot research topic. There were many research studies conducted on Twitter as it is one of the most widely used social media. Previous studies have approached the problem as a tweet-level classification task where each tweet is classified as positive, negative or neutral. However, getting an overall sentiment might not be useful to a business organizations which are using Twitter for monitoring consumer opinion of their products/services. Instead, it is more useful to determine specifically which tweets where users are happy or unhappy about. This paper proposes the discovery of Twitter user level interestingness based on relationships such as retweets, reply-mentions and pure-mentions using Google's PageRank algorithm. We conducted experiments and compared the results with hard-marked results by seven annotators.

1. Discovery ofTwitterUser
Interestingness Based on
Retweets, Reply Mentions
and Pure Mentions
Relationships
Ong Kok Chien , Poo Kuan Hoong and Chiung Ching Ho
Faculty of Computing Informatics, Multimedia University Cyberjaya.
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2016 International Conference on Information in Business andTechnology Management (I2BM)

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Outline
Introduction
Objective
Methods
Results
Summary
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Introduction
 Explore the graph relationships between
Retweets (RT), Reply-Mentions, (RM) and Pure-
Mentions (PM)
 Compare the ranking with hand-marked (HM)
ranking by seven (7) annotators
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Twitter
 Maximum 140 characters microblogging site.
 “ATweet is an expression of a moment or idea. It can
contain text, photos, and videos. Millions ofTweets are
shared in real time, every day.”
Reply
Retweet
Favorite
Hashtags
https://about.twitter.com/what-is-twitter/story-of-a-tweet
.com
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Objectives  To rankTwitter users using Page Rank.
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Methods
 Link-based ranking algorithms (PageRank)
 Twitter Users as Nodes.
 Relationships as Edges.
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Example
 PageRank (PR)
 E.g.: BackLinks inWebsites - Referring back to OriginalContent.
- Sergey Brin & Larry Page (1998). The anatomy of a large-scale hypertextual Web search engine.
Image extracted from Wikipedia
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Example
Minister ofYouth & Sports
Khairykj
shatyrah2 AyenSanji
8 https://twitter.com/Khairykj/status/410964119521460224

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Architecture
Twitter
Streaming API
Configure
Keywords
1 JSON raw
data
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3 HiveQL 4 UnixScript
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Keywords
 HyppTV
 Streamyx
 UMobile
 Unifi
 Yes4G
 Celcom
 xpaxsays
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Basic
Statistics
Dataset
 TotalTweets : 7,931
 After discard non-native Retweets: 7,922
 EnglishTweets (language=en): 2,229
 Unique RT pairs of users: 512
 Unique PM pairs of users: 620
 Unique RM pairs of users: 545
 Unique Full-Mention (FM) pairs of users: 1,154
1st Feb 2015 -> 7th Feb 2015
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Categories of
Tweets
Tweets are categorized into the following categories:
 (1) News - products/company info;
 (2) Advertisements - promotion;
 (3) Business - special offers;
 (4) Jokes - funny/pranks content;
 (5) Questions - seeking for answers/response;
 (6) Answers - response to a question (@mentions);
 (7) Statement - Complaints/comments/feedback;
 (8) Conversation - response to another tweets; and
 (9) Irrelevant - not related to telco products/services.
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Interestingness
score schema
The interestingness score schema was set from the range 0 to 4:
 0 = Irrelevant;
 1 = Less Interesting/Informative;
 2 = Interesting/Informative;
 3 = Quite Interesting/Informative; and
 4 =Very Interesting/Informative
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Results
 Scored an average informative/interestingness score of 1.33 out of
4 by our 7 annotators from 2 iterations.
 Agreement amongst 7 annotators after 2 iterations for 9
categories was 62.27% and score (between 0-4) was 51.64%.
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Results
Rank HM RM PM FM RT
1 Asianadotmy Azwnrafi Zaynneutron Zaynneutron Zaynneutron
2 Zulhusnia Lauravinzant Shahril_Wokay
2
Ndiarzali88 Ndiarzali88
3 IzRijap TuneTalk Azwnrafi TuneTalk UniFiEdge
4 Socasnov FirdausAzil TuneTalk Lauravinzant TuneTalk
5 Pjolll Alliebnorman
d
Alliebnormand Azwnrafi Asianadotmy
6 uk_htc FookHeng_Le
e
Twtwanitaa FirdausAzil uk_htc
7 FookHeng_Lee Pjolll Lauravinzant FookHeng_Le
e
HyppWorld
8 TuneTalk Ndiarzali88 FirdausAzil Twtwanitaa Zulhusnia
9 NurIllihazwani UniFiEdge FookHeng_Lee Pjolll IzRijap
10 Shahril_Wokay
2
HyppWorld Pjolll Alliebnorman
d
FookHeng_Le
e
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• RT shows the closest match of ranking sequence as compared to RM and PM.
• For the case of RM and PM, RM appears to be a better match to the HM sequence.

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Summary
 A PR graph relationships analysis of how RT,
RM and PM impact the perception of user-level
informative/interestingness, validated with HM
evaluations.
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FutureWork  Further evaluation to be conducted using
different weightages of RT, RM and PM.
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