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.

github

twitter

website

Social

OpenChat

# OpenChat Active channels (weekly) Methodology

## 1. Overview

The weekly active channels metric measures the number of unique OpenChat channels that had at least one user engagement activity within a rolling 7-day window. This metric tracks channel-level engagement on the OpenChat social platform, helping understand the breadth of active community discussions and channel utilization over time. The calculation uses a rolling window approach to provide a smoothed view of channel activity patterns.

## 2. Data sources

- **OpenChat event data**: Raw event logs from the OpenChat platform's event system capturing user interactions
- **Platform engagement events**: User activities including message sending, message editing, reaction adding, and message tipping
- **Chat metadata**: JSON payload data containing chat identifiers, chat types, and interaction context
- **Internet Computer blockchain data**: Chain identification data since OpenChat operates on the Internet Computer network

## 3. Step-by-step calculation

### Component A: Event deduplication and filtering
- **Purpose**: Clean and filter raw OpenChat events to focus on relevant channel engagement activities
- **Logic**: Start with raw OpenChat platform events and remove duplicates using a window function partitioned by timestamp, user, event name, source, and JSON payload content. Filter the events to include only engagement activities: `message_sent`, `message_edited`, `reaction_added`, and `message_tipped` events. These represent the core user interaction types that indicate active channel participation.
- **Key fields**: Event `timestamp`, `name` (event type), `user` (participant identifier), `source`, and `payload` (JSON metadata)

### Component B: Chat activity extraction
- **Purpose**: Extract channel-specific activities from the filtered engagement events
- **Logic**: Process the filtered engagement events to extract chat identifiers and types from the JSON payload data. Truncate timestamps to daily grain for consistent time-based aggregation. Extract `chat_id` and `chat_type` fields from the event payload JSON. Filter the results to include only activities where the chat type is 'channel', excluding direct messages, group chats, and other chat types. Assign all activities to the Internet Computer chain and classify them under the social market sector.
- **Key fields**: Daily truncated `timestamp`, `chat_id` (channel identifier), `chat_type` (set to 'channel'), `chain_id` (set to 'internetcomputer')

### Component C: Rolling window creation
- **Purpose**: Create 7-day rolling windows to capture weekly active channel patterns
- **Logic**: Generate a continuous datespine covering all days from the first channel activity to the previous day. For each date in the datespine, create a 7-day rolling window by generating timestamps for that date plus the previous 6 days. Join these rolling windows with the channel activities to identify all channels that had activity within each 7-day period. This approach ensures that a channel active on any day within the 7-day window is counted as active for that window.
- **Key fields**: Window `timestamp` (end date of 7-day period), `chat_id` (channel identifier), daily activity `timestamp`

### Final aggregation
- **Combining components**: Group the windowed channel activities by timestamp and various dimensional breakdowns, then count distinct channel identifiers within each 7-day window
- **Currency conversion**: Not applicable for this count-based metric
- **Time aggregation**: Uses rolling 7-day windows with daily reporting frequency, where each day represents the count of unique channels active in the 7-day period ending on that date
- **Final calculation**: Count the number of distinct `chat_id` values within each 7-day rolling window, creating separate aggregations for project-level (all channels), product-level, and chain-level breakdowns. Apply standardized metric formatting to ensure consistent schema and fill any missing dates with zero values to maintain continuous time series data.

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

Unique active channels over a rolling 7-day window.

OpenChatCHAT

Active channels (weekly)

Active channels (weekly)

Competitive landscape