Map Narratives Faster: qualitative analysis of social media narratives

Evidano is an AI-powered qualitative data analysis platform that helps teams ingest, analyze, and export narrative findings from social media.

The June 30, 2026 PLoS ONE paper introduces ST-TAN, a joint sentiment-topic-temporal model that achieved narrative-structure F1 = 0.87 on a 120, 000-tweet dataset and kept cross-event drops small (≈5.75%).

• Source: PLoS ONE.
• ST-TAN results: narrative F1 = 0.87, narrative integrity = 85.3%, sentiment accuracy = 89%, topic accuracy = 90% on the TEND dataset (n = 120, 000).
• Practical fit: this approach is suited to public opinion monitoring, crisis communications, UX feedback analysis, and other workflows that need reproducible timelines and cross-segment comparisons.

ST-TAN jointly models sentiment and topic with a temporal attention layer to reconstruct ordered narrative units from fragmented social posts.

• Preprocessing: the study splits timelines into 24-hour temporal stages and segments narrative units using a semantic similarity threshold of 0.5.
• Encoder: RoBERTa-base (12 layers, 768 hidden, 12 heads); LoRA is used to reduce fine-tuning parameters by approximately 90%.
• Losses: the model combines a sentiment-weighted cross-entropy, focal loss for sparse narrative labels, and a temporal consistency loss to enforce chronological plausibility.
• Annotation: three annotators produced Cohen’s kappa scores of narrative 0.85, sentiment 0.82, and topic 0.79.
• Evaluation: training set 96k tweets (7 events), validation 12k (1 event), test 12k (2 held-out events).

This checklist lists seven steps to move from raw social data to validated narrative findings.

• 1) Collect: scrape posts and timestamps for the target event, and keep reply/retweet relationships.
• 2) Clean: normalize emojis, remove noise, and convert timestamps to relative hours.
• 3) Segment: split data into 24-hour stages and group by semantic similarity using a threshold of approximately 0.5.
• 4) Label (seed): annotate 500–1, 000 narrative units for role, event, sentiment and topic for a seed set.
• 5) Automate: run thematic and sentiment models and temporal ordering in Evidano; review AI-suggested narrative tuples.
• 6) Validate: compute narrative integrity, spot-check misordered sequences, and refine thresholds.
• 7) Report: export thematic hierarchies, co-occurrence networks and a timed narrative timeline for stakeholders.

ST-TAN shows joint sentiment-topic-temporal modeling materially improves automated narrative reconstruction on social media, demonstrated on a 120, 000-tweet dataset (Published June 30, 2026; narrative F1 = 0.87; narrative integrity = 85.3%).

• Try Evidano for free to import a sample event and run a 7-step pilot.
• Ethics note: these tools are intended for research and aggregate monitoring; ensure anonymization and human review before operational use.