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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
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
Seminar: Social Network 2.0 Canberra, Tuesday 6 Dec 2016
By Xue Li – The University of Queensland 2
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/
Seminar: Social Network 2.0 Canberra, Tuesday 6 Dec 2016
By Xue Li – The University of Queensland 3
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
Seminar: Social Network 2.0 Canberra, Tuesday 6 Dec 2016
By Xue Li – The University of Queensland 4
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
Seminar: Social Network 2.0 Canberra, Tuesday 6 Dec 2016
By Xue Li – The University of Queensland 5
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
Seminar: Social Network 2.0 Canberra, Tuesday 6 Dec 2016
By Xue Li – The University of Queensland 6
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)
Seminar: Social Network 2.0 Canberra, Tuesday 6 Dec 2016
By Xue Li – The University of Queensland 7
Slides copied from HYDATA (海云数据)
Example - HYDATA.com: IoTIoTIoTIoT with Social Mediawith Social Mediawith Social Mediawith Social Media:“Flight information”
22
Slides copied from HYDATA (海云数据)
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
Seminar: Social Network 2.0 Canberra, Tuesday 6 Dec 2016
By Xue Li – The University of Queensland 8
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
5/12/2016 University of Queensland, Australia 32
Seminar: Social Network 2.0 Canberra, Tuesday 6 Dec 2016
By Xue Li – The University of Queensland 9
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
Seminar: Social Network 2.0 Canberra, Tuesday 6 Dec 2016
By Xue Li – The University of Queensland 10
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
Seminar: Social Network 2.0 Canberra, Tuesday 6 Dec 2016
By Xue Li – The University of Queensland 11
• 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
30
40
50
60
70
80
90
Mo
n A
pr
23
17
:00
:00
EST
20
12
Mo
n A
pr
23
19
:00
:00
EST
20
12
Mo
n A
pr
23
21
:00
:00
EST
20
12
Mo
n A
pr
23
23
:00
:00
EST
20
12
Tu
e A
pr
24
01
:00
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EST
20
12
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e A
pr
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03
:00
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EST
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12
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e A
pr
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05
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e A
pr
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07
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12
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e A
pr
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09
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e A
pr
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e A
pr
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e A
pr
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e A
pr
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e A
pr
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e A
pr
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e A
pr
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:00
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EST
20
12
We
d A
pr
25
01
:00
:00
ES
T 2
01
2
We
d A
pr
25
03
:00
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ES
T 2
01
2
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d A
pr
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05
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d A
pr
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07
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2
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d A
pr
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d A
pr
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11
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ES
T 2
01
2
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d A
pr
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13
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ES
T 2
01
2
We
d A
pr
25
15
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T 2
01
2
We
d A
pr
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ES
T 2
01
2
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d A
pr
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T 2
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2
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d A
pr
25
21
:00
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ES
T 2
01
2
We
d A
pr
25
23
:00
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ES
T 2
01
2
Th
u A
pr
26
01
:00
:00
EST
20
12
Th
u A
pr
26
03
:00
:00
EST
20
12
Th
u A
pr
26
05
:00
:00
EST
20
12
Th
u A
pr
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07
:00
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EST
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u A
pr
26
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EST
20
12
Th
u A
pr
26
11
:00
:00
EST
20
12
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
Seminar: Social Network 2.0 Canberra, Tuesday 6 Dec 2016
By Xue Li – The University of Queensland 12
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)
Seminar: Social Network 2.0 Canberra, Tuesday 6 Dec 2016
By Xue Li – The University of Queensland 13
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
Seminar: Social Network 2.0 Canberra, Tuesday 6 Dec 2016
By Xue Li – The University of Queensland 14
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
Seminar: Social Network 2.0 Canberra, Tuesday 6 Dec 2016
By Xue Li – The University of Queensland 15
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.
Seminar: Social Network 2.0 Canberra, Tuesday 6 Dec 2016
By Xue Li – The University of Queensland 16
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!!!
5/12/2016 University of Queensland, Australia 63
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
Seminar: Social Network 2.0 Canberra, Tuesday 6 Dec 2016
By Xue Li – The University of Queensland 17
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
Seminar: Social Network 2.0 Canberra, Tuesday 6 Dec 2016
By Xue Li – The University of Queensland 18
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
Seminar: Social Network 2.0 Canberra, Tuesday 6 Dec 2016
By Xue Li – The University of Queensland 19
Overview of Faculty
73Thanks!