Discussion Search Graph

Overview

The Discussion Share Graph measures how the share of conversation around specific topics, brands, or products evolves over time across social platforms such as TikTok.

TickerTrends analyzes large volumes of social posts, categorizes them by topic or entity, and calculates each one’s share of total discussion within a defined dataset. This approach captures how attention is distributed across competing narratives in real time.

Rather than tracking engagement or sentiment alone, the graph focuses on relative attention, showing what proportion of the overall conversation each topic occupies. Increases in discussion share often reflect rising awareness, stronger narrative momentum, or increased creator-driven amplification

Historical Length

Coverage typically extends multiple years back depending on platform data availability, allowing users to analyze how narratives evolve across different market cycles and product lifecycles.

Granularity

Data is processed at daily granularity, enabling users to observe rapid shifts in attention driven by viral content, news events, or product releases.

Update Frequency

Updated daily with a short delay, ensuring near real-time visibility into changing social dynamics.

Methodology

TickerTrends processes large-scale social data and applies classification models to group posts by topic, brand, or product. Each post contributes to a broader dataset of total discussion volume.

The model then calculates the proportion of total discussion attributed to each entity over time. The output reflects relative share of conversation, allowing users to track how attention shifts across competing topics.

This is designed to capture directional changes in narrative and attention, not absolute mention counts.

Example Visualization

Use Cases

Identify emerging trends and topics gaining social traction. Track shifts in narrative momentum across competing products or brands. Detect early attention signals before they translate into measurable demand. Combine with Market Share and Search data to understand how attention converts into behavior.