May 1, 2024
Updated November 11, 2026
24 minute read
Social media analytics is the process of gathering, analyzing, and interpreting data from social media platforms to inform business decisions and understand online conversations. It involves tracking a wide array of metrics to gain insights into audience behavior, brand perception, and the effectiveness of social media campaigns. At its core, this field helps organizations understand what is being said about them, their competitors, and their industry, and then use that understanding to achieve specific goals. This discipline sits at the intersection of data analysis, communication, and marketing, offering a powerful lens through which to view the digital landscape.
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Find a path to becoming a Social Media Analytics. Learn more at:
OpenCourser.com/topic/lmmuou/social
Reading list
We've selected 18 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Social Media Analytics.
Graph theory, game theory, and information cascades form the mathematical bedrock of modern social network analysis. Easley and Kleinberg explain homophily, strong and weak ties, PageRank, and viral diffusion dynamics with exceptional clarity for data scientists.
Programmatic data acquisition, social graph construction, and natural language processing come together through reproducible Python notebooks. Russell and Klassen demonstrate how to harvest public APIs, analyze community centrality, and extract entity relationships from messy user-generated streams.
Mathematical formulations of community detection, link prediction, influence maximization, and behavior analytics anchor this foundational reference. Zafarani, Abbasi, and Liu bridge network science and machine learning to model how information propagates across online populations.
Business intelligence and econometric modeling meet social listening in this applied guide to digital platform data. Kumar and Qiu examine how organizations quantify word-of-mouth impact, detect misinformation, and translate engagement metrics into operational decisions.
Transformer-based embeddings, zero-shot classification, and neural topic modeling have revolutionized how analysts process millions of social posts. Alammar and Grootendorst provide visual, code-first instruction on clustering short-form text and extracting nuanced sentiment at scale.
Strategic measurement frameworks help marketing and analytics teams move beyond vanity metrics to actionable attribution and brand health tracking. Goncalves outlines how to structure social listening taxonomies, benchmark campaign ROI, and communicate insights to stakeholders.
End-to-end Python workflows walk practitioners through scraping social feeds, cleaning noisy text, and visualizing interaction graphs with NetworkX. Krystyanczuk and Chatterjee cover topic modeling, sentiment scoring, and audience segmentation across major social networks.
Seven layers of social media analytics—text, networks, actions, hyperlinks, mobile, location, and search—provide a structured pedagogical taxonomy. Case studies and lab exercises equip students to align analytical techniques with concrete organizational goals.
Lexicon-based and machine learning approaches to polarity detection gain accuracy when combined with social network homophily and relational context. Pozzi, Fersini, and Messina detail irony detection, emotion classification, and opinion mining across networked communities.
Enterprise social intelligence programs require systematic governance, influencer identification, and crisis monitoring workflows. Kohirkar and Ganis demonstrate how analytics teams integrate unstructured social conversations with customer relationship management pipelines.
Production-ready Python blueprints address the core NLP challenges of social analytics, including hashtag tokenization, named entity recognition, and semantic search. Albrecht, Ramachandran, and Winkler provide reusable templates for analyzing customer feedback and public forums.
Distributed stream processing and graph mining architectures enable researchers to analyze high-velocity social media datasets. Ozdemir and Cakirtas explore trend detection, spam filtering, and large-scale behavioral telemetry across contemporary cloud platforms.
Connecting audience discovery, text analysis, and visualization into a cohesive three-stage framework, Laeeq Khan guides marketers and analysts through modern social listening. Chapters address sentiment evaluation, network mapping, and AI-assisted platform interpretation without heavy coding prerequisites.
Empirical network experiments reveal why behavioral adoption requires wide bridges and clustered reinforcement rather than simple viral reach. Damon Centola provides social data scientists with a rigorous sociological framework for modeling online movements and norm diffusion.
Cross-platform user alignment, heterogeneous graph representation, and temporal activity modeling form the core of this research monograph. He and Nelakurthi present algorithmic methods for predicting user engagement, churn, and anomalous behavior.
Bridging relational graph structure and computational linguistics, Colladon and Vestrelli show how combining text mining with network centrality exposes brand perception and discourse dynamics. Analysts learn practical metrics like the Semantic Brand Score to quantify influence across large social corpora.
Public sector communication, digital commerce, and civic participation generate diverse social datasets requiring specialized analytical lenses. Marques and Sousa compile applied studies examining how organizations ethically extract insight from online discourse.
Behavioral psychology explains the underlying drivers of social sharing—social currency, triggers, emotion, public visibility, practical value, and storytelling. Jonah Berger equips quantitative analysts with qualitative hypotheses to test when evaluating viral content.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/lmmuou/social
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