advancing influencer and sentiment analysis on …...propagation of information throughout a social...
TRANSCRIPT
Slide 1
Advancing Influencer and Sentiment Analysis on Social Networks:
An introduction to IHPC’s Strategic Social Systems Programme
Presented by: Boon Kiat Quek, Ph.D.
Scientist, Computational Social Cognition & Technical Lead, Strategic Social Systems Institute of High Performance Computing, A*STAR [email protected]
Slide 2
Strategic Social Systems (SSS)
About Us A new research programme in IHPC focusing on Translational Social Sciences R&D with the aid of advanced data analysis and computational technologies to promote sustainable business, social and urban success.
People An interdisciplinary team of 15 scientists and collaborators with experience in social psychology, cognitive science, artificial intelligence, behavioral science, economics, data mining, computer engineering, and information systems.
Partners SSS is currently seeking & supporting partners in ICT, financial services, transportation, and goverment agencies. We continue to seek motivated collaborators for forging strategic partnerships
Slide 3 SSS is currently engaging partners in ICT, financial services, transportation, as well as govt agencies.
SMILE Business Domain
SHINE Healthcare Domain
Consumer facing and service-oriented companies: banks, fashion, food & beverages, personal care
Health promotion agencies, infectious disease control centers, hospitals
SPICE Public Domain
Public facing institutions: MNCs, IHLs, government agencies
Strategic Public Information and Communication Enhancement
Social Marketing InteLligence Enhancement
Social Healthcare INformation Enhancement
Key Enabling Technologies: Psychographic analysis (advanced profiling and segmentation) Social media analysis and monitoring (influencer tracking, fine-grained sentiment analysis, misinformation detection, opinion mining) Communication strategy recommendation and decision-support
Strategic Social Systems (SSS)
Slide 4
IHPC’s Strategic Social Systems Team Social and Behavioral Sciences
Advanced Data Analysis
Rick Goh PhD., Electrical and Computer Engineering
Lu Sifei MSc., Computer Architecture
Sebastian Feller PhD., Linguistics
Noraini Rahman B.A., Sociology /Psychology
Richard Shang PhD., Information Systems
Yang Yinping PhD., Information Systems
Wang Zhaoxia PhD., Computer Science
Kayo Sakamoto PhD., Engineering (Cognitive Science)
Ilya Farber PhD., Cognitive Science and Philosophy
Quek Boon Kiat PhD., Integrative Sciences and Engineering
Martin Saerbeck PhD., Industrial Design
*ED, Dy ED IHPC are endorsing directors in our key initiatives. Potential contributions of health domain researchers Su Yi, Xiuju also discussed.
Computational & Software Technologies
Jerry Ping PhD., Information Systems (expected)
Slide 5
Strategic Social Systems: SPICE Sentiment & Insight Analysis* Analysis & Identification of Key Influencers*
Constructive feedback
Sentiment Dimensions
050
100150200250300350400450500
12 A
M2
AM
4 A
M6
AM
8 A
M10
AM
12 P
M2
PM4
PM6
PM8
PM10
PM
Affective
Pos Aff
Neg Aff
Anxiety
Anger
Sadness
Insightful
All
SPICE Team (IHPC)
*work in progress
Slide 6
Capability Development (SPICE) Social media monitoring and social network analysis Identifying key influencers and opinion leaders
Identifying main topics, issues, and opinions
Identifying purpose-specific comments (e.g., feedback)
Fine-grained sentiment analysis
Psychographic analysis of users
Making inferences about future trends vis-à-vis the above
Communication strategy recommendation Identifying users into specific groups with different goals
Messages tailored to achieve goals specific to each group
Slide 7
Key influencers: Why should we care? Influencers and Opinion Leaders “Individuals who have the power to affect purchase decisions
of others because of their (real or perceived) authority, knowledge, position, or relationship.” ~ businessdictionary.com
Ability to reach large segments of the online community
Opinions & sentiments of influencers spread easily to others
Could span across multiple social network services
Identifying key influencers as a means of: Managing spread of information (including misinformation)
Improving marketing of products and services
“Recruiting” early adopters
Slide 8
Key Influencers: How to identify? Step 1: Generating networks from social relationships Inferring relationships from user generated content
Twitter: mentions, replies, “retweets”
Facebook: friendship links, affiliations, interests, “Likes”
Inferring influence as a function of these relationships
Advantages: availability of network analysis methods for characterising relationships between and across nodes
@mrbrown
@miyagi
@STcom
@....
Mentioned by
Slide 9
Key Influencers: How to identify? Step 2: Locating key influencers via various metrics Ranking nodes based on centrality
Degree - based on the number of neighbours
Closeness - based on how easily a node can reach other nodes
Betweeness - based on extent to which a given node lies along shortest paths between two other nodes
Eigenvector – score of a node based on connections to high scoring nodes e.g., PageRank (Google Inc.),
Alpha Centrality (Bonacich & Lloyd, 2001)
Ranking nodes based on other factors or network properties E.g, message frequency, nature of messages, psychographic
features, past behavior patterns
Slide 10
Visualizing Influencer Analysis
Influential users play critical roles in the propagation of information throughout a social network.
Identification of key influencers as a step towards better community engagement.
Current influence based on twitter content collected for a given topic (blue links)
Potential influence based on twitter users’ recent posts on twitter; to uncover latent channels through which information could be propagated. (red links)
Slide 11
Identify latent communication channels through which information could be propagated For a given topic, some links might not have been uncovered
if no actions were performed upon message receipt. But could well be implicated on a different topic in future.
Thus, influence of some users could potentially be higher even though for the current topic their influence is low
Potential / Latent Influence
Topic A Topic B
Dennis Rodman visits N.Korea
Slide 12
Ongoing Research Sentiment Analysis Beyond simple valence polarity (e.g., positive, negative,
neutral)
Grounded in psychological theories of emotions
Integrating influencer analysis with sentiment analysis Why? Knowing which influencers evoke which kinds of
sentiment/emotions allows different actions to be tailored: Misinformation management -- getting to the source, and
mitigating viral flow
“Recruiting” early adopters of products and services
Open ended issues with potentially new insights Influence x Sentiment x Topics x Opinions x Time
Slide 13
Human behavior analysis and modeling from multiple sources of data using computational and social sciences know-how:
Consumer psychographic analysis Characterizing consumer lifestyles, personalities
Deriving consumer insights for product design, targeted marketing, and personalized consumer engagement
Brand-centric social media analysis Monitoring, tracking and analyzing sentiment and key influencers and
opinion leaders on social media
Uncovering consumers’ attitudes towards products and services for enhancing social media marketing and brand management
Opportunities for Collaboration
Supplementary Information
Slide 16
Our approach Approaches attempted Degree centrality (InDegree, OutDegree, or Both)
Weighted degree centrality
“Weighted PageRank” Adaptation of PageRank
Takes number/frequency of tweets into account
Power iteration
∑+−=+j
j
jij
iii InDeg
kxkwW
kxkx)()(
||1)()1()1( ,αα
WPR of node i Smoothing constant Weights of all outbound links
Weight of outbound link from i to j WPR of node j
In-degree of node j
Time instance
Slide 17
Network Generation Starting with a Twitter query in mind, e.g., “MRT”
Gather tweets from Twitter’s API For each tweet T,
Node1 T.senderName If T.text contains “@”, “RT @” Node2 twitter handler from “@” Network.addNode(Node1) Network.addNode(Node2) Network.addLink(Node1, Node2)
Slide 18
Future explorations To look into alternative methods for assessing influence TwitterRank (Weng et al., 2010)
k-shell decomposition (Kitsak et al., 2010)
Enrich influencer analysis with other factors e.g., individual’s sentiment, frequency of tweeting, number of followers, etc.
Slide 19
Some Issues and Questions Issues and Questions Other metrics for assessing influence
Evaluation of the above metrics
Any other important but overlooked issue to be analyzed
Finding the right research questions to address vs. applying available methods to meet needs? i.e., implement known methods such as TwitterRank instead of
trying to come up with new ones