OpenChat users can send messages to each other containing tokens such as ICP. Users can upgrade to Diamond membership to have access to extra or enhanced features.

OpenChat is a chat application running end-to-end on the Internet Computer blockchain.

CHAT tokenholders own and govern the protocol.

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OpenChat

# OpenChat Active chats (weekly) Methodology

## 1. Overview

Active chats weekly measures the number of distinct chats that had at least one user interaction during a rolling 7-day period on the OpenChat platform. This metric indicates chat engagement levels by tracking which conversations remain active over weekly time windows. The calculation uses a rolling window approach where each day's value represents the count of unique chats that had activity in the current day plus the previous 6 days.

## 2. Data sources

- **OpenChat platform events**: Timestamped user interaction events from the OpenChat social platform on the Internet Computer blockchain
- **Event payload data**: JSON-structured metadata containing chat identifiers, chat types, and interaction details
- **User activity logs**: Records of message sending, editing, reactions, and tipping activities that indicate chat participation
- **Platform metadata**: Chat classification data and user engagement tracking information

## 3. Step-by-step calculation

### Component A: Event deduplication and standardization
- **Purpose**: Clean and standardize raw OpenChat event data to remove duplicates and prepare for analysis
- **Logic**: Extract events from OpenChat platform data and apply deduplication logic using row numbering partitioned by timestamp, user, event name, source, and JSON-serialized payload. This ensures each unique user interaction is counted only once, even if recorded multiple times in the raw data.
- **Key fields**: `timestamp` for timing, `name` for event type, `user` for actor identification, `payload` for chat metadata

### Component B: Chat activity event filtering
- **Purpose**: Identify events that represent meaningful chat activity and extract relevant chat information
- **Logic**: Filter events to include only chat interaction types that indicate active conversation participation: message sending (`message_sent`), message editing (`message_edited`), reaction additions (`reaction_added`), and message tipping (`message_tipped`). Extract `chat_id` and `chat_type` from the JSON payload to identify which specific chats had activity. Truncate timestamps to daily granularity and add platform context including market sector classification as social media and Internet Computer chain identification.
- **Key fields**: `chat_id` extracted from payload, `chat_type` for conversation classification, daily `timestamp` boundaries

### Component C: Rolling 7-day window creation
- **Purpose**: Create 7-day rolling time windows to capture weekly chat activity patterns
- **Logic**: Generate a complete datespine covering all days from the first recorded event to yesterday to ensure no gaps in the time series. For each day in the datespine, create a 7-day lookback window by generating timestamps from 6 days prior through the current day. Join this windowed datespine with chat activity events where the activity date falls within each window period. This approach ensures that each day's metric value represents chat activity across a full 7-day rolling period.
- **Key fields**: `timestamp` for the reporting date, `joinday` for activity dates within the window, `chat_id` for identifying active conversations

### Final aggregation
- **Combining components**: Aggregate the windowed chat activities to count distinct active chats across multiple dimensional breakdowns: project-level (all OpenChat activity), product-level (by product classification), and chain-level (by Internet Computer network). Each breakdown uses the same underlying data but provides different aggregation perspectives.
- **Currency conversion**: Not applicable for this engagement metric as it measures conversation counts rather than financial values
- **Time aggregation**: Daily reporting with each day representing a 7-day rolling window of activity. The rolling nature means values are calculated continuously rather than in discrete weekly periods.
- **Final calculation**: Count distinct `chat_id` values within each 7-day window period, grouped by the reporting timestamp and dimensional breakdown. Apply standardization through the `finalize_metric_active_chats_weekly` macro which handles schema formatting and fills missing days with zero values to maintain complete time series continuity. Apply chain availability filtering to ensure data quality and consistency across the platform's operational periods.

*This methodology was automatically generated by Token Terminal's methodology analyzer.*

Unique active chats over a rolling 7-day window.

OpenChatCHAT

Active chats (weekly)

Active chats (weekly)

Competitive landscape