MODULE 1 – Social Media Analytics: An Overview.
Q1. Short note on Types of Social media risk.
Ans :- Reputational Risk: Negative posts, comments, or misinformation can damage a brand’s
image. Inappropriate content or employee misconduct online can also harm credibility.
Cybersecurity Risk: Phishing, malware, or hacking through social media platforms can compromise
data. Weak passwords or unsecured accounts increase vulnerability.
Compliance Risk: Violating regulations (e.g., GDPR, HIPAA) or platform policies can lead to fines or
account suspension. Misleading ads or undisclosed sponsorships are common issues.
Operational Risk: Overreliance on social media for communication or sales can disrupt operations if
platforms face outages or algorithm changes.
Privacy Risk: Oversharing personal or sensitive information can lead to data breaches or identity
theft. Third-party apps may also misuse user data.
Content Risk: Inaccurate, offensive, or controversial content can alienate audiences or spark
backlash, especially if not aligned with brand values.
Q2. Write down core Characteristics of social media.
Ans:- Entertaining: Social media grabs attention with fun, exciting, or enjoyable content like videos,
memes, or stories. It keeps users coming back because it’s a source of amusement or relaxation.
Aspirational: It shows users what they could achieve or become, like a better lifestyle, career, or
personal growth. Think of influencers sharing success stories or fitness journeys that motivate others
to aim higher.
Actionable social media offers content that prompts users to take specific actions, whether it’s
participating in a challenge, sharing an idea, or engaging in campaigns. This characteristic
motivates users to not just consume content but to actively engage and contribute.
Joinable: Social media platforms are all about connecting with others. Whether it's following friends,
joining groups with shared interests, or interacting with brands, the sense of belonging to a group
or network is very important. Features like comments, direct messages, and shared spaces make
social media "joinable."
Pay Off: Social media gives users different kinds of rewards like fun, learning, meeting new people,
or sometimes even money. People use these platforms hoping to get something valuable in return
for their time and activity.
Q3. Write down the types of social media.
Ans:- Social Networking: are online places where people connect with each other. Users can like,
share, comment on posts, and follow friends or businesses. For brands, these sites help create
awareness, build a group of followers, and bring visitors to their websites. By sharing interesting and
helpful content, brands can build good relationships with customers that may lead to sales.
Examples: Facebook, Instagram, Twitter, LinkedIn, TikTok, Snapchat.
Photo and Image Sharing: These sites focused on sharing photos and visual content. Popular
platforms include Instagram, Pinterest, and Flickr.
Video Sharing:.Many people like watching videos more than reading or looking at pictures. Websites
like YouTube, TikTok, and Vimeo are very popular because they show lots of videos.
MODULE 1 – Social Media Analytics: An Overview.
Videos are easy to watch since you don’t have to read or scroll. They are also great for teaching
things, like how-to guides and tutorials. This makes videos very popular on social media.
Social messaging apps: Social messaging apps are a type of social media that allows users to
communicate in real time. One of the most popular messaging apps WhatsApp has more than 200
billion monthly users!
Examples: Facebook Messenger, Twitter DMs, Google Business Messenger, WhatsApp, and WeChat.
Microblogging platforms: Microblogging is a type of social media marketing that allows users to post
short updates or messages. Twitter is the most famous example of microblogging, allowing users to
tweet in 140 characters or less.
Earlier, Twitter was the main microblogging site, but now LinkedIn and Tumblr are also popular for
sharing short posts.
Examples : Twitter, Tumblr, Pinterest and LinkedIn
Community Blogging Sites: Platforms that enable users to create and share blog content within a
community. Medium and WordPress are examples.
Live Streaming: Platforms that facilitate real-time broadcasting of video content. Notable examples
are Twitch and Facebook Live.
Social Curation and Bookmarking Sites: Platforms that allow users to collect, organize, and share
content from the web. Examples include Pinterest and Flipboard.
Q3. Short note on Challenges of social media analytics.
Ans:- 1. Data Volume and Variety:
• Challenge: Social media generates massive amounts of data daily, including text, images,
videos, audio, and user interactions. This sheer volume is difficult to process and analyze
efficiently.
• Challenge: The data comes in various formats (unstructured text, visuals, etc.) from different
platforms with their own unique structures and APIs, making it hard to combine and
compare.
2. Data Access and Privacy:
• Challenge: Social media platforms often have restrictions on data access due to privacy
concerns and their own business interests. This can limit the scope and depth of analysis.
• Challenge: Regulations like GDPR and CCPA require careful handling of user data, adding
complexity to data collection and analysis processes.
3. Identifying Meaningful Metrics:
• Challenge: It's easy to get caught up in "vanity metrics" (likes, followers) that don't
necessarily reflect actual business impact. Identifying and tracking metrics that align with
specific business goals (e.g., engagement, reach with target audience, sentiment related to
brand) is crucial but can be difficult.
4. Real-time Analysis and Speed:
MODULE 1 – Social Media Analytics: An Overview.
• Challenge: Social media conversations and trends can change rapidly. Analyzing data in real-
time to identify emerging issues or opportunities requires sophisticated tools and quick
processing capabilities.
5. Data Quality and Noise:
• Challenge: Social media data can be noisy, containing irrelevant information, spam, and bot
activity, which can skew analysis and lead to inaccurate insights. Cleaning and filtering this
data is essential but time-consuming.
6. Sentiment Analysis Accuracy:
• Challenge: Determining the true sentiment (positive, negative, neutral) behind social media
posts, especially considering nuances like sarcasm and slang, can be difficult for automated
tools.
Q4. Explain briefly the seven layers of social media analytics with an example.
Ans:- social media has at least seven layers of data (shown in Figure 2). Each layer can give useful
information that businesses can use.
1. Text
2. Networks
3. Actions
4. Hyperlinks
5. Mobile
6. Location
7. Search engines
MODULE 1 – Social Media Analytics: An Overview.
Text: This layer focuses on the textual content shared on social media, including posts, comments,
and captions. Analyzing text helps identify trends, sentiments, and topics of interest.
Example: A brand monitoring mentions of its products can analyze customer feedback to improve
offerings.
Networks: Social media network analytics extract, analyze, and interpret personal and professional
social networks, for example, Facebook, Friendship Network, and Twitter.
Network analytics aims to identify influential nodes (e.g., people and organizations) and their
position in the network.
Action: Social media actions analytics deals with extracting, analyzing,and interpreting the actions
performed by social media users, including likes, dislikes, shares,mentions, and endorsement.
Actions analytics are mostly used to measure popularity,influence, and prediction in social media.
Mobile: This layer looks at how people use social media on their mobile phones. It’s important
because it shows how users behave and what they like.
Example: An app might check if users spend more time on social media using their phone or
computer to plan better ads.
Hyperlink: Hyperlink analytics means studying the links shared on social media.
It helps understand where internet traffic is coming from or going to.
For example: It can show which websites are sending visitors to a page, or where people go after
leaving that page.
Location: Location analytics, also known as spatial analysis or geospatial analytics, is concerned with
mining and mapping the locations of social media users, contents, and data.
Search Engines: This layer looks at how people find social media content through search engines.
Knowing how to use keywords helps more people see the content.
Example: A brand uses the right keywords in its posts and profile so it shows up higher in search
results.
Q5. Write down the types of Social Media.
Ans:- Like any business analytics, social media analytics can take three forms:
1) descriptive analytics, 2) predictive analytics, and 3) prescriptive analytics.
1) DESCRIPTIVE ANALYTICS: Descriptive analytics is mostly focused on gathering and describing
social media data in the form of reports, visualizations, and clustering to understand a business
problem. Actions analytics (e.g., no. of likes, tweets, and views) and text analytics are examples of
descriptive analytics. Social media text (e.g., user comments), for example, can be used to
understand users’ sentiments or identify emerging trends by clustering themes and topics. Currently,
descriptive analytics accounts for the majority of social media analytics.
2) Predictive Analytics: This type uses historical data and statistical algorithms to forecast future
outcomes. It helps businesses understand trends and user behavior.
Example: Using past engagement patterns to predict which types of content will perform well in
upcoming campaigns.
3) Prescriptive Analytics: This type provides recommendations based on data analysis, helping
businesses make informed decisions. It suggests actions to optimize performance.
MODULE 1 – Social Media Analytics: An Overview.
Example: Analyzing engagement data to recommend the best times to post for maximum visibility
and interaction.
Q5. Differentiate Social Media vs. Traditional Business Analytics.
Ans:-
Q6. Explain in detail the Social Media Analytics Life Cycle.
Ans: STEP 1: IDENTIFICATION The identification stage is the art part of social media analytics and is
concerned with searching and identifying the right source of information for analytical purposes.
Data for analytics will come from business-owned social media platforms. While some data for
analytics, will also be harvested from nonofficial social media platforms. The source and type of data
to be analyzed should be aligned with business objectives. Framing the right question and knowing
what data to analyze is extremely crucial in gaining useful business insights.
STEP 2: EXTRACTION Once a reliable and minable source of data is identified, next comes
the science of extraction stage. The type (e.g., text, numerical, or network) and size of data will
determine the method and tools suitable for extraction. Small-size numerical information, for
example, can be extracted manually (e.g., going through your Facebook fan page and counting
likes and copying comments), and a large-scale automated extraction is done through an API
(application programming interface).
Two important issues to bear in mind here are the privacy and ethical issues related to mining
data from social media platforms. Data extraction practices should not violate a user’s privacy
and the data extracted should be handled carefully. While all social media platforms have their
privacy policies in place, to be on the safe side it is advisable to craft your own social media
privacy policy. Your policies should explicitly detail social media ownership in terms of both
accounts and activities such as individual and page profiles, platform content, posting activity,
data handling and extraction, etc.
STEP 3: CLEANING This step involves removing the unwanted data from the automatically
extracted data. Some data may need a lot of cleaning, and others can go into analysis directly.
MODULE 1 – Social Media Analytics: An Overview.
In the case of the text analytics, for example, cleaning, coding, clustering, and filtering may be
needed to get rid of irrelevant textual data using natural language processing (NPL). Coding
and filtering can be performed by machines (i.e., automated) or can be performed manually by
humans. For example, DiscoverText combines both machine learning and human coding
techniques to code, cluster, and classify social media data.
STEP 4: ANALYZING At this stage the clean data is analyzed for business insights.
Depending on the layer of social media analytics under consideration and the tools and
algorithm employed, the steps and approach you take will greatly vary. For example, nodes in
a social media network can be clustered and visualized in a variety of ways depending on the
algorithm employed. The overall objective at this stage is to extract meaningful insights
without the data losing its integrity. While most of the analytics tools will follow you through
the step-by-step procedure to analyze your data, having background knowledge and an
understanding of the tools and its capabilities is crucial in arriving at the right answers.
STEP 5: VISUALIZATION In addition to numerical results, most of the seven layers of
social media analytics will also result in visual outcomes. The science of effective visualization
known as visual analytics is becoming an important part of interactive decision making
facilitated by solid visualization. Effective visualization is particularly helpful with complex
and huge data because it can reveal hidden patterns, relationships, and trends. It is the effective
visualization of the results that will demonstrate the value of social media data to top
management. Depending on the layer of the analytics, the analysis part will result in relevant
visualizations for effective communication of results. Depending on the type of data, different
types of visualization are possible, including the following.
Prof. Harshali Bhuwad Department of Computer Engineering
Network data (with whom)—network data visualizations can show who is connected to whom.
For example, a Twitter following-following network chart can show who is following whom.
Topical data (what)—topical data visualization is mostly focused on what aspect of a
phenomenon is under investigation. A text cloud generated from social media comments can
show what topics/themes are occurring more frequently in the discussion.
Temporal data (when)—temporal data visualization slice and dice data with respect to a time
horizon and can reveal longitudinal trends, patterns, and relationships hidden in the data.
Google trends data, for example, can visually investigate longitudinal search engine trends
Geospatial data (where)—geospatial data visualization is used to map and locate data, people,
and resources.
Other forms of visualizations include trees, hierarchical, multidimensional (chart, graphs, tag
clouds), 3-D (dimension), computer simulation, infographics, flows, tables, heat maps, plots,
etc.
STEP 6: INTERPRETATION Interpreting and translating analytics results into a meaningful
business problem is the art part of social media analytics. This step relies on human judgments
to interpret valuable knowledge from the visual data. Meaningful interpretation is particularly
important when we are dealing with descriptive analytics that leave room for different
interpretations. Having domain knowledge and expertise are crucial in consuming the obtained
results correctly. Two strategies or approaches used here can be
1) producing easily consumable analytical results and
2) improving analytics consumption capabilities.
The first approach requires training data scientists and analysts to produce interactive and easy-
to-use visual results. And the second strategy focuses on improving management analytics
consumption capabilities.
MODULE 1 – Social Media Analytics: An Overview.
