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 (monthly) Methodology

## 1. Overview

Active channels monthly measures the number of distinct OpenChat channels that had at least one user interaction within a rolling 30-day window. This metric tracks channel engagement and activity levels on the OpenChat social platform, providing insights into community vitality and platform usage patterns. The calculation uses a rolling window approach where each day represents the count of unique channels that were active during the current day plus the previous 29 days.

## 2. Data sources

The metric relies on the following data sources:

- **OpenChat events API data**: Raw event logs from the OpenChat social platform containing user interactions and platform activities
- **Internet Computer Protocol activity records**: Blockchain-based events and transactions from the Internet Computer network where OpenChat operates  
- **Chat messaging and interaction events**: User-generated activities including message sending, editing, reactions, and tipping within OpenChat channels
- **Platform engagement tracking data**: Structured event data with timestamps, user identifiers, event types, and JSON payloads containing chat metadata

## 3. Step-by-step calculation

### Component A: Event deduplication and filtering
- **Purpose**: Clean and filter raw OpenChat events to focus on meaningful channel activities
- **Logic**: Raw OpenChat events are deduplicated using a row numbering approach that partitions by timestamp, user, event name, source, and JSON payload content. Only the first occurrence of each unique event combination is retained. Events are then filtered to include only activity-indicating interactions: `message_sent`, `message_edited`, `reaction_added`, and `message_tipped` events
- **Key fields**: `timestamp` for event timing, `name` for event type identification, `user` for actor identification, `payload` containing structured chat metadata including `chat_id` and `chat_type`

### Component B: Channel activity identification  
- **Purpose**: Extract channel-specific activities from the broader set of chat interactions
- **Logic**: The filtered events are processed to extract chat metadata from JSON payloads. The `chat_id` field identifies specific chat instances while `chat_type` distinguishes between channels and private groups. Only events where `chat_type` equals 'channel' are retained, excluding private group activities. Timestamps are truncated to daily boundaries and standardized metadata is added including market sector and chain identifiers
- **Key fields**: `chat_id` for unique channel identification, `chat_type` for channel vs group classification, daily truncated `timestamp` for consistent time boundaries

### Component C: Rolling window construction
- **Purpose**: Create 30-day rolling windows for each day to capture channel activity over the trailing month
- **Logic**: A datespine is generated covering all days from the earliest channel activity to the previous day. For each date in the datespine, a 30-day window is constructed by creating an array of the current date plus the previous 29 days. This creates overlapping windows where each day represents a unique 30-day measurement period. Channel activities are then joined to each window day, capturing all channels that had activity during any day within the 30-day period
- **Key fields**: `timestamp` for the window end date, `joinday` for each day within the 30-day lookback period

### Component D: Multi-dimensional aggregation
- **Purpose**: Calculate active channel counts across different analytical dimensions
- **Logic**: For each 30-day window, distinct `chat_id` values are counted to determine the number of unique channels with activity. This counting is performed across three dimensional breakdowns: overall project level (all channels combined), by product identifier, and by blockchain network. Each breakdown maintains the same time window logic but groups results differently for analytical flexibility
- **Key fields**: `active_channels_monthly` as the distinct count of `chat_id` values, dimension identifiers for grouping breakdowns

### Final aggregation
- **Combining components**: The three dimensional breakdowns (project-level, product-level, and chain-level) are combined using union operations to create a unified result set with dimension labels
- **Currency conversion**: Not applicable - this is a count-based metric measuring channel activity rather than financial values
- **Time aggregation**: Each daily timestamp represents the count of channels active during the trailing 30-day period ending on that date, creating overlapping monthly measurements updated daily
- **Final calculation**: The final metric value is the distinct count of `chat_id` values for channels that had at least one qualifying interaction event within each 30-day rolling window, with missing days filled with zero values to maintain complete time series coverage

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

Unique active channels over a rolling 30-day window.

OpenChatCHAT

Active channels (monthly)

Active channels (monthly)

Competitive landscape