The document provides an overview of social media analytics, explaining its significance in gathering insights from social media platforms. It discusses types of analytics, including exploratory, content, and social network analytics, and highlights the importance of analyzing connections and interactions within social networks. Additionally, it introduces various tools and platforms developed for efficient social media analysis, emphasizing the potential of such analytics in understanding societal trends and opinions.

1. Social Media Analytics
Muhammad Rifqi Ma’arif
Pusat Studi dan Layanan Analitik Data (PuSLAD)
Fakultas Teknik dan Teknologi Informasi
Universitas Jenderal Achmad Yani Yogyakarta

2. Social Media
• Currently the internet is a place which people can
• Consume and create content
• Interact wit society
• To think loud, share ideas and informations
• A new way of socializing
• Broadly social media is anything that engages people and start dialog
• A powerfull tools to change the world order
• And so on..

3. Social Media Platforms
• Blogs
• Microblogs
• Wikis
• Video Sharing
• Document Sharing
• Social Bookmarks
• Podcasts
• Screencasts
• Social Networks
• Currated Q&A

4. What people do in Social Media?
• Create information/content
• Share information/content
• Express opinion (own post, comments, likes)
• Thus....
• The pulse of society can be found in social media in real-time. Analyzing
social media contents and people interaction can helps us to have better
understanding of current and predict the future.

5. Social Media Analytics
Social media analytics is the practice of gathering data from blogs and social
media websites, such as Twitter, Facebook, Instagram etc.. and analyze that data
to gather insightfull information.
Example Social Media Analytics Use Case
• Gather customer opinion to support marketing and customer service activities
• Summarizing citizen response to a new policy or citizen polarity in politics
• Monitoring adverse effect of drugs usage

6. Anatomy of Social Media Data
• Basic Feature
• Time of post creation
• Post Owner
• Enggagement
• Geolocation
• etc....
• Advance Feature
• Entity
• Events
• Sentiment
• Keywords
• etc.....

7. Types of Analytics
• Exploratory Analytics
• Content Analytics
• Social Network Analytics

8. Exploratory Analytics
• Post Statistics
• Number of Post
• Trend Post
• Most popular post
• Most popular user/people
• Sentiment Proportion
• Enggagement (Number of comments,
likes, share)
• Demographic Analysis
• And so on...

9. Exploratory Analytics

10. Exploratory Analytics

11. Exploratory Analytics

12. Exploratory Analytics

13. Content Analytics
• Word/Hashtag Cloud
• Word/Hashtag Co-occurence Matrix
• Sentiment Analysis
• Topic Modelling
• Topic Stream
• And so on..

14. Word/Hashtag Cloud

15. Word/Hashtag Co-occurence

16. Sentiment Analysis
Sentiment analysis or opinion mining
refers to the application of natural
language processing, computational
linguistics, and text analytics to identify
and extract subjective information in
source materials
• Issues on sentiment analysis
• Context
• Granularity
• Aspect-oriented
• Sarcasm
• Multi sentiment

17. Topic Modeling
• Is a type of statistical model for discovering the hidden semantic structures in a text
body that occur in a collection of documents.
• Represents as a set of keywords which construct the idea of the text.
• Algorithms: Latent Semantic Analysis (LSA), Latent Dirichelet Analysis (LDA)
LDA Model:
Topic #0: 0.006*"would" + 0.006*"one" + 0.004*"said" + 0.003*"new" + 0.003*"two" + 0.003*"time" + 0.003*"could" + 0.002*"may" + 0.002*"man" + 0.002*"also"
Topic #1: 0.005*"one" + 0.005*"would" + 0.004*"said" + 0.003*"new" + 0.003*"could" + 0.003*"made" + 0.003*"two" + 0.003*"time" + 0.002*"first" + 0.002*"may"
Topic #2: 0.005*"one" + 0.005*"would" + 0.005*"said" + 0.004*"could" + 0.003*"time" + 0.002*"two" + 0.002*"even" + 0.002*"new" + 0.002*"way" + 0.002*"first"
Topic #3: 0.007*"would" + 0.005*"one" + 0.004*"could" + 0.004*"said" + 0.003*"first" + 0.003*"new" + 0.003*"may" + 0.002*"two" + 0.002*"time" + 0.002*"man"
Topic #4: 0.006*"one" + 0.004*"said" + 0.004*"would" + 0.003*"could" + 0.003*"new" + 0.003*"like" + 0.003*"even" + 0.002*"two" + 0.002*"time" + 0.002*"may"
Topic #5: 0.007*"one" + 0.006*"would" + 0.004*"time" + 0.003*"could" + 0.003*"may" + 0.003*"man" + 0.003*"said" + 0.002*"like" + 0.002*"new" + 0.002*"two"
Topic #6: 0.007*"one" + 0.003*"may" + 0.003*"would" + 0.003*"could" + 0.003*"time" + 0.003*"new" + 0.003*"first" + 0.003*"two" + 0.003*"said" + 0.002*"man"
Topic #7: 0.005*"one" + 0.004*"would" + 0.003*"new" + 0.003*"said" + 0.003*"first" + 0.003*"man" + 0.003*"two" + 0.003*"may" + 0.003*"state" + 0.003*"could"
Topic #8: 0.007*"one" + 0.004*"would" + 0.003*"may" + 0.003*"time" + 0.003*"new" + 0.003*"two" + 0.002*"said" + 0.002*"mrs." + 0.002*"many" + 0.002*"also"
Topic #9: 0.007*"would" + 0.006*"one" + 0.004*"said" + 0.004*"could" + 0.003*"new" + 0.003*"like"

18. Topic Modeling

19. Topic Modeling

20. Topic Stream
• Advance version of topic modelling by adding the time dimension

21. Social Network Analysis
• Social network analysis is a method to analyze
the connections based on interaction across
individuals within community.
• It can be applied across disciplines—there are
social networks, political networks, electrical
networks, transportation networks, and so on.
• Social Network reveals how information/goods/
material transmits, propagate and consumed by
society/community

22. Social Network Data
• The unit of interest in a network are the combined sets of actors and their relations.
• We represent actors with nodes and relations with lines.
• Edges can represent interactions, flows of information, similarities/affiliations, or social relations
• In general, a relation can be Binary or Valued and Directed or Undirected
a
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Undirected, binary Directed, binary
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Undirected, Valued Directed, Valued
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23. Network Data Structure
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Undirected, binary Directed, binary
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Adjacency List
a b
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Arc List

24. Social Network Characteristics
• There is cluster of nodes
• A group of individuals with
stronger relation comparing to
those beyond the group
• Node can have a weight (or size)
based on various criteria.
• There is a nodes which more
influential to the others (Centrality)

25. Centrality Analysis
• At the individual level, one dimension of position in the network can be captured
through centrality.
• Conceptually, centrality is straight forward: we want to identify which nodes are
in the ‘center’ of the network.
• In practice, identifying exactly what we mean by ‘center’ is somewhat
complicated.
• Three basic concept of centrality: Degree, Closeness and Betweeness

26. Degree Centrality
• A node’s (in-) or (out-)degree is the number of links
that lead into or out of the node
• In an undirected graph they are of course identical
• Often used as measure of a node’s degree of
connectedness and hence also influence and/or
popularity
• Useful in assessing which nodes are central with
respect to spreading information and influencing
others in their immediate ‘neighborhood’

27. Closeness Centrality
• It is a measure of reach, i.e. The speed with which information
can reach other nodes from a given starting node.
• How fast information can spread from one node to every other
node

28. Betweeness Centrality
• Shows which nodes are more likely to be in
communication paths between other nodes.
• Also useful in determining points where the
network would break apart (think who
would be cut off if nodes 3 or 5 or node A
would disappear)
• Edge with high beetweness centrality score
are potentially in very powerful positions

29. Interpretation of Centrality
• What would degree, closeness, and betweenness centrality reveal?
• Degree ⇒ Most friends ⇒ Most popular person
• How many people can this person reach directly?
• Closeness ⇒ Can quickly reach the whole group (directly or indirectly)
• How fast can this person reach everyone in the network?
• Relevant if we want to quickly spread information in the network
• Betweenness ⇒ Power in the transmission of information
• How likely is this person to be the most direct route between two people in the network?
• Relevant if we want to influence communication between groups

30. Comparing Centrality
• Different measures target different notions of
importance
• In a friendship network, degree centrality would
correspond to who is the most popular kid.
• Closeness centrality would correspond to who is
closest to the rest of the group,. Useful for
spreading information
• Betweenness would tell us about graph “cut
points”, edges whose deletion will cause multiple
connected components

31. Tools for Social Media Analytics
• Why Tools?
• Large number of
unstructured data
• Group of SMA Tools
• Data collection tools
• Data engineering
• Data analytics
• Data visualization
• And so on...

32. Type of Tools
• For Programmer
• Prog. Languanges
• Libraries
• For Non Programmer
• Desktop based tools
• Cloud service

33. Cloud Analytic Service
• Keyhole
• Sprout Social
• Brandwatch
• Sonar Platform
• Drone Emprit
• NolimitID
• ..and many more...

34. Medi@n Analytics
• An (wanna be) integrated cloud platform for Social
Media Analytics
• Developed by Pusat Studi dan Layanan Analytic
Data (PuSLAD) Universitas Jenderal Achmad Yani
Yogyakarta since 2019.
• The main purpose is to provide a platform for
implementing the results of a research in data
analytic conducted by lecture and/or students.

35. Medi@n Analytics

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