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2026 Social Media Analytics · Sentiment · Topics · Networks · Bots · Engagement

Social Media Analytics Projects

Best final-year topics on social media analytics — sentiment analysis, topic modeling, network and community detection, influencer ranking, bot detection and engagement prediction with Python, NetworkX, Hugging Face and public social datasets.

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Sentiment Topic Modeling Networks Bots · Fake Engagement Applications

Social Media Analytics Projects — From Posts to Insights

Social media analytics turns posts, networks and engagement signals into measurable insights — sentiment trends, topics, influencers and anomalies. Final-year projects that implement clear pipelines with evaluation metrics produce strong, portfolio-ready results.

Below are 80+ topics across sentiment, topic modeling, network analysis, bot detection, engagement prediction and applications, with tools (Python, NetworkX, Hugging Face, scikit-learn) and public datasets (Kaggle, labelled sentiment corpora, sample social graphs).

Python / pandas NetworkX Hugging Face scikit-learn NLTK / spaCy Kaggle Datasets
# Social Media Analytics Project Topic Tools · Datasets
😊 Sentiment Analysis · Emotion
01SentSentiment Classification on Twitter/X Samplesscikit-learn / HF, Kaggle
02SentAspect-Based Sentiment AnalysisspaCy, aspect extraction
03SentEmotion Detection (Joy, Anger, Fear, etc.)Multi-label classifiers
04SentLexicon vs ML Sentiment ComparisonVADER / TextBlob vs models
05SentFine-Tuning BERT for Domain SentimentHugging Face Transformers
06SentMultilingual Sentiment Analysis PilotmBERT / XLM-R
07SentSentiment Trends Over Time (Time Series)pandas, rolling averages
08SentSarcasm and Irony Detection ConceptsSpecialised classifiers
09SentBrand Sentiment Monitoring Dashboard SketchAggregation + plots
10SentHandling Class Imbalance in Sentiment DataResampling, class weights
11SentEmoji and Emoticon Impact on SentimentFeature ablation
12SentCross-Platform Sentiment Consistency StudyTwitter vs Reddit samples
13SentExplainable Sentiment with Attention / SHAPInterpretability tools
14SentReal-Time Sentiment Stream PrototypeStreaming simulation
15SentEvaluation: Accuracy, F1, Confusion MatricesStandard metrics
📌 Topic Modeling · Content Themes
16TopLDA Topic Modeling on Social Postsgensim / sklearn LDA
17TopBERTopic for Dynamic Topic DiscoveryBERTopic, embeddings
18TopHashtag Co-Occurrence and Cluster AnalysisNetworkX, clustering
19TopTopic Evolution Over Time (Dynamic Topics)Time-sliced LDA
20TopKeyword Extraction and TF-IDF Rankingscikit-learn TF-IDF
21TopDocument Clustering of Posts / ThreadsK-means, hierarchical
22TopTopic Coherence Evaluation MetricsNPMI, UMass scores
23TopEvent Detection from Burst of TopicsBurst detection methods
24TopComparative Topic Analysis Across CommunitiesSubreddit / group splits
25TopVisualisation of Topics with Word Clouds / pyLDAvisInteractive plots
26TopShort-Text Topic Models for TweetsBiterm / specialised LDA
27TopSeeded / Guided Topic ModelingPrior-constrained models
🕸️ Network Analysis · Influence · Communities
28NetSocial Graph Construction from Mentions / FollowsNetworkX, edge lists
29NetCentrality Measures (Degree, Betweenness, PageRank)NetworkX algorithms
30NetCommunity Detection (Louvain / Label Propagation)NetworkX / community
31NetInfluencer Ranking by Engagement and ReachComposite scores
32NetHomophily and Assortativity AnalysisAttribute correlation
33NetInformation Diffusion / Cascade ModelsIndependent cascade concepts
34NetEgo Network Analysis of Key UsersSubgraph extraction
35NetLink Prediction on Social GraphsSimilarity / ML features
36NetVisualisation with Gephi / NetworkX DrawingLayout algorithms
37NetTemporal Network Evolution StudySnapshot comparison
38NetBridge Nodes and Information BrokersBetweenness focus
39NetMultiplex Networks (Follow + Mention Layers)Multi-layer analysis
🤖 Bot Detection · Fake Content · Credibility
40BotBot Account Detection with Behavioural Featuresscikit-learn, feature eng
41BotContent-Based Fake News ClassificationTF-IDF / BERT, labelled sets
42BotPropagation Patterns of MisinformationCascade analysis
43BotCoordinated Inauthentic Behaviour IndicatorsTemporal synchrony features
44BotCredibility Scoring of Sources / AccountsComposite reputation scores
45BotSpam and Promotional Content FiltersClassifiers, rules
46BotDeepfake / Synthetic Media Awareness SurveyLiterature + detection overview
47BotEvaluation of Bot Detection Models (F1, ROC)Standard binary metrics
48BotFeature Importance for Bot vs Human ClassificationSHAP / permutation
📈 Engagement · Prediction · Virality
49EngEngagement Prediction (Likes, Shares, Comments)Regression models
50EngFeature Importance for Viral PostsText + metadata features
51EngOptimal Posting Time AnalysisTemporal patterns
52EngContent Type Performance ComparisonImage vs text vs video
53EngHashtag Effectiveness RankingEngagement lift metrics
54EngUser Engagement Lifecycle ModelingCohort analysis
55EngA/B Testing Concepts for Content VariantsExperiment design
56EngChurn / Inactive User PredictionClassification models
57EngCross-Platform Engagement CorrelationMulti-platform samples
58EngEarly Virality Signal DetectionFirst-hour metrics
🏭 Applications · Ethics · Research
59AppElection / Political Discourse Sentiment StudyPublic political corpora
60AppBrand Crisis Detection from Sentiment ShiftsTime-series alerts
61AppCustomer Support Theme Mining from ReviewsTopic + sentiment
62AppAcademic / Research Trend Analysis on SocialHashtag / keyword trends
63AppMental Health Discourse Awareness AnalysisSensitive topic handling
64AppSports Event Fan Sentiment TrackingEvent-window analysis
65AppProduct Launch Buzz MeasurementVolume + sentiment
66AppInfluencer Marketing ROI Proxy MetricsEngagement attribution
67EvalDashboard Design for Social Analytics KPIsPlotly / Streamlit concepts
68EvalReproducible Analysis Pipeline PackageNotebooks, configs, seeds
69ResearchEthical Guidelines for Social Data AnalysisPrivacy, consent report
70ResearchBias and Fairness in Sentiment ModelsDemographic evaluation
71ResearchData Collection and Anonymisation PracticesBest-practice guide
72ResearchOpen Datasets and Benchmarks SurveyKaggle + academic sets
73ResearchEducational Lab: Collect → Clean → Analyse → ReportStudent starter kit
74ResearchCommon Pitfalls in Student Social Analytics ProjectsChecklist design
75ResearchAPI Limits and Sample Bias AwarenessMethodology discussion
76ResearchMulti-Modal Social Content (Text + Image)Fusion concepts
77ResearchLongitudinal Study Design on Social TopicsPanel / cohort design
78ResearchStudent Portfolio: Dashboard + Metrics FiguresFigure pipeline
79ResearchThesis Package: Question → Data → Model → DiscussFull documentation
80ResearchComparison of Classical vs Deep NLP for Social TextBenchmark report
81ResearchPolicy Implications of Automated Social AnalyticsPolicy overview
82ResearchEnd-to-End Capstone: Dataset to Interactive DashboardComplete project arc

Topics use Python, NetworkX, Hugging Face, scikit-learn, NLTK/spaCy and public datasets (Kaggle, labelled sentiment corpora, sample social graphs). Live API access is optional. Contact us for reference material, code, evaluation metrics, university-format report, PPT and viva Q&A for any topic above.

Why Choose Us for Social Media Analytics Projects?

Bangalore-based guidance for BE, BTech and MTech students working on sentiment, networks, topics and engagement analytics.

Sentiment Analysis

Lexicon and ML sentiment, aspect-based models, emotion detection and trend analysis.

Topic Modeling

LDA, BERTopic, hashtag clusters and temporal topic evolution with coherence metrics.

Network Analysis

Centrality, community detection, influencer ranking and diffusion models with NetworkX.

Bot & Credibility

Bot detection, fake news classification and credibility scoring with behavioural features.

Frequently Asked Questions — Social Media Analytics

Top topics include sentiment analysis on Twitter/X or Reddit, topic modeling (LDA/BERTopic), social network analysis and community detection, influencer ranking, bot/fake account detection and engagement prediction.
Python (pandas, scikit-learn, NetworkX), Hugging Face Transformers, NLTK/spaCy, Gephi concepts; public datasets from Kaggle, Twitter Academic samples, Reddit dumps and labelled sentiment corpora.
Yes. Packages include reference material, analysis code, evaluation metrics, dataset notes, university-format report, PPT and viva Q&A.
No. Most student projects use publicly available archived datasets, Kaggle collections or sample exports. Live API access is optional and not required for a complete academic project.