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 groups (weekly) Methodology

## 1. Overview

Active groups weekly measures the number of distinct OpenChat groups that had at least one activity (message, edit, reaction, or tip) within a rolling 7-day period. This metric tracks engagement levels and community vitality across OpenChat's social messaging platform on the Internet Computer blockchain. The calculation uses a sliding window approach to provide smooth, continuous measurement of group activity trends.

## 2. Data sources

- **OpenChat platform events**: Structured event data from OpenChat's messaging platform containing user interactions, message activities, and group metadata
- **Internet Computer blockchain data**: Underlying blockchain interaction logs that capture OpenChat platform activities
- **Event payload data**: JSON-formatted metadata containing chat identifiers, chat types, user information, and activity details
- **Platform activity logs**: Real-time event streams capturing message sends, edits, reactions, and tips within chat groups

## 3. Step-by-step calculation

### Component A: Event deduplication and filtering
- **Purpose**: Clean and filter raw OpenChat events to identify relevant group activities
- **Logic**: Raw events are deduplicated using a combination of timestamp, user, event name, source, and payload content. Events are filtered to include only messaging activities: `message_sent`, `message_edited`, `reaction_added`, and `message_tipped`. JSON payload data is parsed to extract the `chat_id` and `chat_type` fields. Timestamps are truncated to daily grain and standardized.
- **Key fields**: `timestamp` (activity date), `name` (event type), `user` (actor), `payload` (JSON metadata), `chat_id` (group identifier), `chat_type` (group vs direct message)

### Component B: Group activity isolation
- **Purpose**: Filter chat activities to focus only on group conversations
- **Logic**: Activities are filtered to include only those where `chat_type` equals 'group', excluding direct messages and other chat types. This ensures the metric specifically measures group engagement rather than all platform activity. Each record represents a daily activity occurrence within a specific group.
- **Key fields**: `chat_id` (group identifier), `chat_type` (filtered to 'group'), `timestamp` (daily grain), `chain_id` ('internetcomputer')

### Component C: Rolling window creation
- **Purpose**: Create 7-day sliding windows for measuring weekly activity
- **Logic**: A datespine is generated covering all days from the first recorded activity through yesterday. For each date in the datespine, a 7-day window is created by generating an array of the current date plus the previous 6 days. This creates overlapping 7-day periods that slide forward one day at a time, ensuring smooth weekly measurements without gaps in the time series.
- **Key fields**: `timestamp` (window end date), `joinday` (individual days within the window)

### Component D: Activity window matching
- **Purpose**: Match group activities with their corresponding 7-day windows
- **Logic**: Group activities are joined with the rolling windows based on matching the activity date with any day within each window. This means an activity on day X will contribute to 7 different weekly windows (the windows ending on days X through X+6). The join creates multiple records per activity, enabling the sliding window calculation.
- **Key fields**: `timestamp` (window end date), `chat_id` (group identifier), `timestamp_grain` ('day')

### Component E: Dimensional aggregation
- **Purpose**: Count distinct active groups across multiple dimensional breakdowns
- **Logic**: Active groups are counted by taking the distinct count of `chat_id` values within each 7-day window. Three dimensional breakdowns are created: by project (overall OpenChat), by product (null for OpenChat), and by chain (Internet Computer). Each breakdown aggregates the same underlying data but with different dimensional attributes for reporting flexibility.
- **Key fields**: `active_groups_weekly` (distinct count of `chat_id`), `dimensions` (breakdown type), `chain_id`, `product_id`

### Final aggregation
- **Combining components**: The three dimensional breakdowns are combined using union operations to create a unified result set with multiple reporting perspectives
- **Currency conversion**: No currency conversion required as this is a count metric
- **Time aggregation**: Daily timestamps represent the end date of each 7-day rolling window, creating a continuous daily series of weekly measurements
- **Final calculation**: The final metric value is the distinct count of group chat IDs that had at least one qualifying activity (message, edit, reaction, or tip) within each 7-day rolling window period

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

Unique active groups over a rolling 7-day window.

OpenChatCHAT

Active groups (weekly)

Active groups (weekly)

Competitive landscape