Faster Insights: Social Media Narrative Analysis

ST-TAN jointly models sentiment, topic, and temporal attention to automate narrative recognition on social media, and it shows measurable gains on a held-out-event benchmark. On the Twitter Event Narrative Dataset (TEND, n=120, 000) ST-TAN reports narrative F1 0.87 and narrative integrity 85.3%, improving baselines by roughly 8–11%. Qualitative teams can reproduce ST-TAN-style insights with a six-step pipeline: ingest, clean and segment, auto-code, validate, visualize, and share.

• On TEND (120, 000 tweets) ST-TAN achieves narrative F1 0.87 and narrative integrity 85.3%, outperforming baselines by approximately 8–11%.
• ST-TAN’s core innovations are joint sentiment-topic training, attention-weighted topic modeling, and a temporal-consistency loss that preserves narrative ordering.
• A practical six-step workflow (ingest, clean/segment, auto-code, validate, visualize, share) can reproduce ST-TAN-style outputs using tools like Evidano.

This section explains ST-TAN in plain English: ST-TAN is a Sentiment-Topic fused Temporal Attention Network built on a RoBERTa-base encoder that jointly models sentiment, topic, and temporal structure to detect narrative units in social streams. The authors build ST-TAN from a RoBERTa-base encoder plus three fused modules: an emotion-aware head, a topic attention head weighted by sentiment, and an attention-based temporal modeling head; key innovations include joint sentiment-topic training, a temporal consistency loss, and preprocessing that segments narrative units by 24-hour temporal windows plus semantic similarity.

• Dataset and design: the authors use TEND, a dataset of 120, 000 tweets across 10 public events, and they use held-out events for the train/val/test split to evaluate cross-event generalization.
• Performance highlights: ST-TAN reports narrative F1 0.87 (about +8.75% versus the best baseline), narrative integrity 85.3% (+10.2%), sentiment accuracy around 0.89, and topic accuracy around 0.90.
• Robustness: ST-TAN degrades less under simulated noise, with high-noise narrative F1 about 0.76, attributed to targeted preprocessing and sentiment-weighted losses.

This section summarizes the core pipeline and why each part matters: the pipeline first cleans and segments text into narrative units using a 24-hour temporal threshold plus semantic similarity, then encodes segments with RoBERTa, applies an emotion-aware head, runs sentiment-weighted topic attention, and applies temporal attention with a temporal-consistency loss during LoRA fine-tuning.

• Cleaning and segmentation: narrative-unit segmentation uses 24-hour windows and semantic similarity threshold (≥0.5).
• RoBERTa encoder: the model uses a 12-layer, 768-d RoBERTa base encoder.
• Joint modeling benefits: modeling sentiment and topic together helps detect topic transitions because sentiment shifts often signal changes in topic; removing temporal attention drops temporal-position F1 by about 14.9%.
• Annotation quality: Cohen’s kappa greater than 0.79 across tasks supports stable supervised signals for multi-task training.

This section lists a six-step workflow to reproduce ST-TAN-style insights in Evidano.

1) Ingest: scrape the public social stream or upload a CSV of posts and timestamps using Evidano.

2) Clean and segment: apply Evidano preprocessing, including emoji mapping and spell correction, then segment by 24-hour windows and semantic clustering.

3) Auto-code: run combined sentiment and thematic analysis with Evidano AI models to get initial labels and confidence scores.

4) Validate: sample low-confidence or short-text cases, correct labels with human annotators, and push corrections back to retrain models.

5) Visualize: generate co-occurrence networks, timeline views, and narrative integrity metrics to trace arcs and high-impact nodes.

6) Share and act: export a stakeholder brief and set alerts for negative sentiment spikes or new topic emergence.

This section summarizes the main findings and the next step: ST-TAN demonstrates that integrating sentiment, topic, and temporal modeling materially improves automated narrative recognition on social media, as reported in PLOS ONE on 30 Jun 2026. For qualitative teams, the practical takeaway is to combine dimensions early in the pipeline and to bake temporal consistency into sequence-level analyses.

Ready to test a pipeline? Ingest your social feed or transcripts into Evidano and run a joint sentiment, topic, and temporal analysis in hours instead of weeks. Try Evidano for free.