challenging questions - meetupfiles.meetup.com/14535342/xue-li-social-network-2.0.pdf · seminar:...

Seminar: Social Network 2.0 Canberra, Tuesday 6 Dec 2016

By Xue Li – The University of Queensland 1

Social Network 2.0 – from sharing

experiences to sharing values

Xue LiThe University of Queensland

6 Dec 2016

http://staff/itee.uq.edu.au/xue

[email protected]

Team members:Abdulqader Almars, Tony Chen, Rocky Chen,

Hongxu Chen, Jingwei Ma, Mingyang Zhong,

Xing Zhao , Lin Wu

Canberra Data Scientists - SeminarCanberra Data Scientists - Seminar

Challenging Questions

• Search Engines: searching for facts or opinions?

• Social Media 2.0: sharing experiences or values?

• Data Ownership: Data = Value?

• Connecting Social Networks with Internet of

Things?

5/12/2016 University of Queensland, Australia 2Social Media 2.0

Internet of Things

E-Commerce

Social Networks

3

Youtube Link: https://www.youtube.com/watch?v=NrK2hiH3q9I

• 2014 Premier’s Awards for Open Data Winner of Best use of open data• Microsoft StartUp Q Award Winner

On the way of seeking opinions …

• "I do not like to state an opinion on a

matter unless I know the precise facts."- Einstein, New York Times, August 12, 1945.

4

http://www.asl-associates.com/einsteinquotes.htm



Questions on

Sentiment Analysis & Opinion Mining

• Would opinions from social media data reflect true social

opinions? If yes, how much? What kind?

• Would opinion mining tools be generic or domain specific?

Yes, or both?

• Would social opinions manipulable? (spamming opinions)

• How to obtain social opinions from social media effectively

and efficiently?!

5

Twitter sentiment versus Gallup Poll of

Consumer Confidence

Brendan O'Connor, Ramnath Balasubramanyan, Bryan R. Routledge, and Noah A.

Smith. 2010. From Tweets to Polls: Linking Text Sentiment to Public Opinion Time

Series. In ICWSM-2010

https://web.stanford.edu/class/cs124/lec/sentiment.pptx 6

Challenging Examples

• “The food was great but the service was awful.”– Object with features: [Restaurant, Food, Services]

• “I really think I shouldn’t be here.”– Negative to the implied event or location of the current

speaker.

• “You are terrible! : - )”– Positive to an object: [a friend of speaker]

• “Stop the boat!”– Domain specific: negative to ALP & positive to LNP in

Australian government election in 2013.

University of Queensland, Australia 7 8

http://www.pewinternet.org/



Social Network Topics

9

0 10 20 30 40 50 60 70 80

Music and Movies

Community issues

Sports

Politics

Religion

Global publics are sharing their views online about a variety of topics. Most

of them use the social network to share opinions about music and movies.

Pew Reaech Center http://www.pewglobal.org/

Social Media and the ‘Spiral of SilenceSpiral of SilenceSpiral of SilenceSpiral of Silence’

“Not only were social media sites not an alternative forum for discussion, social media users were less willing to share their opinions in face-to-face settings.”

Noelle-Neumann, E. (1974). “The Spiral of Silence A Theory of Public Opinion.” Journal of Communication 24(2): 43-51.

http://www.slideshare.net/fullscreen/PewInternet/pew-research-findings-on-politics-and-advocacy-in-the-social-media-era/4

10

11

Twitter Schema

User Tweet

Followed by

Tweets

Re-tweeted

Topics

Candidates



An Intuition: An Intuition: An Intuition: An Intuition: How do we calculate the votes for a state?

•� = �(�� + 1 − � )• f: f(Connectivity_of_an-opinion user)

• p: p(Key-Phrase in the Language Model of an Candidate)

• s: scaling factor, as f and p consider different aspects of the political elections.

• �: for scaling the weights of f and p.

• Given a set of local tweets and its users who are in favour of a candidate (in a state), X is the # of predicted votes.

[WISE2012, WISE 2014]

A Question on discovering the Silent Majority

• One Question: Which newspaper is the most popular one in Hong Kong?

• Another Question: Which newspaper would be mostly read in public transportations during the rush hours in Hong Kong?

Lessons Learned in Predicting 2016 USA Election

Sentiment is reactive but opinion is proactiveSentiment is reactive but opinion is proactive

12

PositiveNegative

LNP

ALP

LNP

ALP

Coal

CoalQLDVotes

QLDVotes

.

.

.

Tony Job unemployed

education cut

Tony Job unemployed

education cut

Weather health

hospital aging care

Weather health

hospital aging care

.

.

.

time

13

Language Models along the timeline



Our Prediction System

17

Social Media 1.0 (current)Social Media 1.0 (current)Social Media 1.0 (current)Social Media 1.0 (current)

• Purpose: • Sharing information and experiences;

• Socializing.

• Players: Individuals, groups and organizations.

• Contents: Text, opinions, pictures, videos,

and information related to the entities.

[1] Scott, John. Social network analysis. Sage, 2012.

[2] Obar, Jonathan A., and Steven S. Wildman.

Social Media Definition and the Governance Challenge-An Introduction to the Special Issue, 2015. 15

Our vision: Social Media 2.0Our vision: Social Media 2.0Our vision: Social Media 2.0Our vision: Social Media 2.0

• Purpose:

Sharing services/values together with experiences;

• Players: Individuals, groups and organizations;

• Contents: Services/values provided by the entities.

People’s feelings, attitudes, opinions, and

believes may affect: Reputation of banks

House pricings

Stock market Prices

Government elections

Tourism of a country

Fashion products,

… 16

Why do we need Social Media 2.0?Why do we need Social Media 2.0?Why do we need Social Media 2.0?Why do we need Social Media 2.0?

17



Core Idea of Social Media 2.0 Core Idea of Social Media 2.0 Core Idea of Social Media 2.0 Core Idea of Social Media 2.0 –––– Data is valueData is valueData is valueData is value

• A place for sharing experiences together with

sharing of values

• A one-stop platform for connecting all Services with

all Consumers

• A gateway for connecting Social networks with the

IoT (Internet of Things)

18

Social Media 2.0 Social Media 2.0 Social Media 2.0 Social Media 2.0 –––– key features key features key features key features

• Opinion based;

• Service centered;

• Data = values (ownership, Data centers ⇒ Storage centres).

19

Linking Social Networks with IoT

• Human centred applications

• Service-to-Consumer match making

• Integration of three-flows:

Data Flow

Workflow,

Cash Flow

• Performance evaluation and benchmarking (feedbacks)

20

Example - HYDATA.com: IoTIoTIoTIoT with Social Mediawith Social Mediawith Social Mediawith Social Media:“People travelling”

24

Slides copied from HYDATA (海云数据)




Example - HYDATA.com: IoTIoTIoTIoT with Social Mediawith Social Mediawith Social Mediawith Social Media:“Flight information”

22


Example - HYDATA.com: IoTIoTIoTIoT with Social Mediawith Social Mediawith Social Mediawith Social Media:“Feedback of white goods”

23

Social media 2.0 Social media 2.0 Social media 2.0 Social media 2.0 –––– ecosystemecosystemecosystemecosystem

24

ExampleExampleExampleExample

25



Our Invention: Opinion Search Engine (OSE)

• Opinion Search Engine (OSE) is a constrained function (f) for finding opinions (instead of finding

Web contents):

� x is a set of Social Network Users (Who).

� y is a set of target objects (What - Topics, organizations, Product & Services, Events, …).

� z is a set of opinions (How - Positive, Negative, Neutral, Like, Love, Hate, etc).

� t t t t is a constraint about time point or time period (When).

� llll is a constraint about a geo-location (Where - an area, a city, a state, …).

• x can be structured as a graph of social communities (Retweeting vs. Friend-of-Friend/follower

networks).

• y can be used to narrow down (partition) the community graph of x.

• OSE uses big data fusion techniques. It offers users a comprehensive bird’s-eye-view on

everything that is happening over social media.

• OSE can be used as a plug-and-play system component to be integrated with Web Engine

systems.

�� , � = �

26

Our Big O-Table

5/12/2016 University of Queensland, Australia 27

Who posted this message (x)?

What message this post is

talking about (y)?

What is the opinion revealed in

this message (z)?

When is this message posted (t)?

Where is this message posted (l)?

�� , � = �

Machine Learning & In-Memory Computing are the key for

Big O-Table

31

DMA with LRU

In-Memory Polymorphic Database

Dashboard: A virtual instrument approach

for OSE Big Data visualization and interaction




Results

Content Layer

Network Layer

Spatial-Temporal Layer

Query on

Object

Opinion Analysis on Objects

Time

Tracking everything - who, where, what, when

30Space = S & Time = t

Computing

tasks at Space s

and Time t

Object and

Feature

Object and

Feature

Who are

talking

Who are

talking

⋮

Postings FCA Lattice

Hot Feature Tree

Ra

nk

ing

Analysed Hot Features

Op

inio

n

Conversation

Friendship

What

Opinions

What

Opinions

31

Time

Snapshots along the timeline

…

Performance

Prediction System

based on Social Media

Analytics

Unique

Language

Model - 1

Unique

Language

Model - 2

Unique

Language

Model - n

Houses Universities Political Parties

(Organizations)

Performance Query

Performance Prediction Result

0

2

4

6

KPI

1

KPI

2

KPI

3

KPI

4

. . .

WWW

. . .

Organization

Performance

Historical

Database

102

Unique

Language

Model (ULM)

of

Organizations

10

1

Spatial-

Temporal

Spectrum (STS)

of Social

Media

103

Social Media

Graphs

104

Raw Social

Media Data &

Meta Data

105

10

0

A Framework of Big Data Fusion with Social Media Analytics

33



Architecture of Data Fusion with Social Media System Architecture

Software Platforms

39 40

Representation of Social Media

How can we extract, represent, and

visualize the features of social media

as whole?

Xue Li, et al (2015) Spatial and Temporal Word Spectrum of Social Media, SIGKDD 2015 Workshop WISDOM, Sydney, 2015,

http://sentic.net/wisdom/2015/li.pdf



• Different users from different geolocations will post different microblog

messages for their local issues.

• Unique local features of social media can be calculated in different time.

• Spatial-Temporal Word Spectrum (STWS) model is a linguistic

fingerprint of a geolocation on social media.

• STWS is a baseline to catch the prominent and statistical features of

microblogs

� to detect emerging local events.

� to guess the location of a user.

� to reveal behavioral features of local users.

• STWS opens a new way of studying social media.

If we need to watch over the social media, but we do

not know what to watch for, what can we do?

http://sentic.net/wisdom/2015/li.pdf 37

Example: When do people go to bed?

5/12/2016 42

South Africa (17/04/2015—23/04/2015) UTC+02:00

Australia (17/04/2015—23/04/2015) UTC+10:00

USA (23/04/2015—29/04/2015) UTC-05:00

(Brisbane location): When do people have bad mood?

0

10

20

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40

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60

70

80

90

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n A

pr

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17

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Bad Message Count

Bad Message Count

Trend of bad messages during two and half days from Brisbane

Rate bad messages: 1.13 (round off to 2) minute per bad message.

Thus, on an average 2 minutes interval is sensible to catch up with abusive words.

Spatial-Temporal Word Spectrum (STWS)

• Calculate temporal TF for all terms in all locations

• Calculate temporal IDF for all terms in all different locations

• Calculate the correlations between Terms, locations, and time

periods.

• STWS becomes a temporal fingerprint of the social media at

that location in a time period.

44http://sentic.net/wisdom/2015/li.pdf



A Sample STWS

45Spatial and Temporal Word Spectrum among words, hours, and regions

The numbers of tweets in different hours of a day

46

The TFs of ‘sleep’ and ‘job’

47

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 230

0.01

0.02

0.03

0.04

0.05

0.06

Hours

Ter

m F

requ

ency

jobsleep

Frequency comparison with the baseline

48

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23-0.02

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

Hours

Wor

d F

requ

ency

policepolice (baseline)drugdrug (baseline)



49

Representation of Social Communities

How can we extract, represent, and

visualize the different social

communities over social networks?

The Outcome: PTO Net (People –Topic – Opinion Network)

Community Profiling -behavior discovery of communities

47

Case 1: Location-Sensitive Emerging

Event detection

51Best Student Paper, at APWeb 2013, Sydney AUSTRALIA

Visualize what is happening

52

Illustration of Emerging Events by our Approach



Locations are important in events

detection• We need to know what real-world events that

can be triggered from within the cyber space.

• We need to know where we expect them or

where they are happening.

• We need to know when (or is) happening

• We may even need to know who are involved

in the event, …

53

We are interested only in emerging

“hotspot” events• An event is something that occurs in a certain place during a particular

interval of time. [http://dictionary.reference.com/browse/event?s=t]

• Emerging event is an event that has significantly increased in the

number of messages but rarely posted in the part.

• A hotspot event is an event where there is a strong

association between event location and user

location.

– User location is a location where the message is sent from.

– Message-referred location is a location mentioned in the message. It could be:

• Event location is a location where the event occurs

• Other locations referred from within micro-blogs,…

54

We discover events from micro-blog

messagesLondon riot August 2011,

(http://www.guardian.co.uk/uk/2011/aug/08/lond

on-riots-tottenham-duggan-blog Access:

26/07/2012)

Everyone from all sides of London meet up at the heart of

London (central) OXFORD CIRCUS!!, Bare SHOPS are gonna

get smashed up so come get some (free stuff!!!) fuck the

feds we will send them back with OUR riot! >:O

We need more MAN then feds so Everyone run wild, all of

london and others are invited! Pure terror and havoc &

Free stuff....just smash shop windows and cart out da stuff

u want! Oxford Circus!!!!! 9pm, we don't need pussyhole

feds to run the streets and put our brothers in jail so tool

up,

Oxford Circus 9pm if u see a fed stopping a brother JUMP

IN!!! EVERYONE JUMP IN niggers will be lurking about, all

blacked out we strike at 9:15pm-9:30pm, make sure ur

there see you there. REMEMBA DA LOCATION!!! OXFORD

CIRCUS!!!

Earthquake in Melbourne, Australia on

20/7/2012 (Twitter)

Anyone feel the tremor in Melbourne?

#earthquake

Another earthquake in Victoria?

And now an earthquake in Melbourne?

(Clearly, in the news world, it never rains,it

pours.) Wtf?

At 7:11pm Melbourne had another

earthquake. An egg I had set out for dinner

rolled off the bench and cracked on the floor.

#wewillrebuild

Earthquake in Melbourne is a hashing topic

now since yesterday. I felt the earth move

under my feet 1 day earlier.

55

Time of emerging

• Emerging event detection

– To detect emerging event, we need to find frequency of messages which are

moving from low to high state.

– Using the average and standard deviation, we can find out the emerging point

like an outlier.

56

Time unit

Fre

qu

en

cy o

f m

ess

ag

es

Current time slots

high

low



Case 2:

Election Prediction

Demonstration Scenario

55

59

The geo-location based sentiment analysis on social networks can reveal the feelings of locals on current

issues. The graph in the top-left corner shows the scores of general feelings of the searched topic (e.g., “Bus

Services”). The graph in the top-right corner shows the social network structure currently active at the

selected location (e.g., St Lucia). The bottom table displays the actual posted messages on the topic.

60

Prediction of Election Events

Dataset: The messages posted by Australian-based users related to the 2013 AustralianFederal Election were collected by Twitter Search API. 808,661 messages (4 Aug – 8 Sep2013) with the user’s initial event query is used for our experiments.



Case 3:Case 3:Case 3:Case 3:

Cyberbullying DetectionCyberbullying DetectionCyberbullying DetectionCyberbullying Detection

Victims of Cyber Bullying

62

http://www.abc.net.au/news/stories/2011/05/30/3231123.htm?site=melbourne

http://dbgm2010.wordpress.com/2010/10/08/lgbt-community-rocked-by-two-more-suicides/

http://www.news.com.au/world-news/online-bully-victim-amanda-todd-still-tormented-in-death/story-fndir2ev-1226497411838

http://www.submitthedocumentary.com/schoolboy-15-found-hanged-at-his-home-was-tormented-by-cyber-bullies/

Who will protect them!!!


http://www.abc.net.au/news/stories/2011/05/30/3231123.htm?site=melbourne

http://dbgm2010.wordpress.com/2010/10/08/lgbt-community-rocked-by-two-more-suicides/

http://www.news.com.au/world-news/online-bully-victim-amanda-todd-still-tormented-in-death/story-fndir2ev-1226497411838

http://www.submitthedocumentary.com/schoolboy-15-found-hanged-at-his-home-was-tormented-by-cyber-bullies/

Bullying network - User

groups are formed based on

the densely interconnected

links (bullying post)

61



Differentiation of victims from predators

• Classify users in various categories of

victimization based on the ranking : severe,

moderate and normal bullying cases.

• Normal category indicates:

• Higher victim ranking and higher predator

ranking

• A user is involved in a interchange and that

may lead to a hostile interchange.

• A receiver of the bullying message replied

through a bullying message.

• Severe category indicates:

• Higher victim ranking and lower predator

ranking,

• Victim is unable to defend himself.

No of users

Rank Predators Victims

I 4 1

II 2 4

III 1 7

IV 1 2

V 2 2

VI 7 1

VII 3 9

VIII 1 8

Detection Techniques

66

Detection

Filter techniques Theory of

communication

model

Statistical based

models

Predator

detection

Cyber bullying

detection

Predator

detection

Link Analysis

HITS

Cyber bullying

detection

Case 4:

Products and Services

Recommendation

Example: Comparative Studies over the

Social Networks

68

� Hot features

� Opinions on those hot features



How to recommend a

product or

service?

69

eBay

Amazon

Processed topics

The feature extraction for mobile phone products

70

Conclusions

• Opinion Search Engine (OSE) is to summarize opinions in

order to predict events

• Social Media 2.0 is a new platform which will bring a silent

revolution to the Internet

• Social Networks need to work with the Internet of Things

• Data fusion, machine learning, and data visualization are

the main technologies.

71

Our Published Work

• Xue Li, et al (2015) Spatial and Temporal Word Spectrum of Social Media, SIGKDD 2015 Workshop WISDOM, Sydney, 2015, http://sentic.net/wisdom/2015/li.pdf

• Sayan Unankard, Xue Li, and Guodong Long (2015) Invariant Event Detection in Social Networks, DASFAA 2015, Hanoi, Vietnam, 20-23 April 2015, The Best System Demo Paper.

• Sayan Unankard, Xue Li, Mohamed A. Sharaf (2014) Emerging Event Detection in Social Networks with Location Sensitivity, WWWJ (World Wide Web Journal http://link.springer.com/article/10.1007%2Fs11280-014-0291-3, July 2014 (ERA A).

• Vinita Nahar, Xue Li, Hao Lan Zhang, and Chaoyi Pang (2014) Detecting Cyberbullying in Social Networks based on Positive Unlabelled Learning, International Journal of Web Intelligence and Agent Systems (WIAS), IOS Press, 11 April 2014.

• Sayan Unankard, Xue Li, Mohamed A. Sharaf, Jiang Zhong, and Xueming Li. (2014) Predicting Elections from Social Networks based on Sub-Event Detection and Sentiment Analysis, In WISE, (Web Information System Engineering), Part II, LNCS8787, pp1-16, Thessaloniki, Greece, 12-14 October 2014.

• Unankard, Sayan, Li, Xue and Sharaf, Mohamed A. (2013). Location-based emerging event detection in social networks. Proceedings. 15th Asia-Pacific Web Conference (APWeb), 2013, Sydney, Australia, (280-291). 4-6 April, 2013. [Best Student Paper]

• Zhao, Peng, Li, Xue and Wang, Ke (2013). Feature extraction from micro-blogs for comparison of products and services. In: Xuemin Lin, et al, Web Information Systems Engineering, WISE 2013 - 14th International Conference, Proceedings. Nanjing, China, (82-91). 13 -15 October 2013. ERA-A

• Vinita Nahar, Xue Li, Chaoyi Pang. Cyberbullying Detection based on Text-Stream Classification. In the Eleventh Australasian Data Mining Conference (AusDM 2013), Canberra, Australia 13-15 November 2013

• S. Unankard, L. Chen, P. Li, S. Wang, Z. Huang, M. Sharaf, and X. Li (2012), On the Prediction of Re-tweeting Activities in Social Networks, WISE 2012 Challenge, WISE 2012 Champion of the Data Mining Track, Cyprus 28-30 Nov., 2012.

• Vinita Nahar, Sayan Unankard, Xue Li, Chaoyi Pang (2012): Sentiment Analysis for Effective Detection of Cyber Bullying. APWeb 2012: 767-774.

72



Overview of Faculty

73Thanks!

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