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 direct chats (weekly) Methodology

## 1. Overview

Weekly active direct chats measures the number of unique direct (one-on-one) chat conversations that had at least one activity within a rolling 7-day period on the OpenChat social platform. This metric tracks engagement in private conversations as opposed to group chats, providing insights into peer-to-peer communication patterns. The calculation uses a rolling 7-day window and applies a division by 2 adjustment as recommended by the OpenChat team to account for the bilateral nature of direct chats.

## 2. Data sources

The calculation relies on the following data sources:
- **OpenChat event logs**: Platform events from the Internet Computer blockchain capturing user interactions within the OpenChat social application
- **Message activity events**: Four types of chat interactions including `message_sent`, `message_edited`, `reaction_added`, and `message_tipped` events
- **Event metadata**: JSON payload data containing `chat_id` identifiers and `chat_type` classifications to distinguish between direct and group conversations
- **Timestamp data**: Event timestamps for daily aggregation and rolling window calculations

## 3. Step-by-step calculation

### Component A: Event collection and deduplication
- **Purpose**: Gather all relevant chat activities and remove duplicates from the raw event stream
- **Logic**: Collect events with names `message_sent`, `message_edited`, `reaction_added`, and `message_tipped` from OpenChat's event system. Apply deduplication using row numbering partitioned by `timestamp`, `user`, `name`, `source`, and JSON-stringified `payload` to ensure each unique event is counted only once
- **Key fields**: Event `timestamp`, event `name`, `user` identifier, `source` system, and `payload` containing chat metadata

### Component B: Chat activity processing  
- **Purpose**: Extract chat information and standardize the data for metric calculation
- **Logic**: Parse the JSON `payload` to extract `chat_id` and `chat_type` fields from each event. Truncate timestamps to day-level granularity for daily aggregation. Add standard classification fields including `market_sector_id` as 'social', `business_segment_id` as 'social', and `chain_id` as 'internetcomputer'
- **Key fields**: Daily `timestamp`, `chat_id` identifier, `chat_type` classification, and standardized dimension fields

### Component C: Direct chat filtering
- **Purpose**: Isolate activities specific to direct (one-on-one) conversations
- **Logic**: Filter the processed chat activities to include only records where `chat_type` equals 'direct', excluding group chats and other conversation types. This ensures the metric focuses specifically on peer-to-peer communication
- **Key fields**: Filtered dataset containing only direct chat activities with preserved `chat_id` and daily `timestamp`

### Component D: Rolling window generation
- **Purpose**: Create 7-day rolling windows for weekly aggregation that don't skip missing timestamps
- **Logic**: Generate a continuous datespine from the minimum activity date to yesterday. For each date in the spine, create joinday records for the current date and the previous 6 days, forming 7-day rolling windows. Join this window structure with the direct chat activities to associate each activity with all relevant weekly periods it should be counted in
- **Key fields**: Window `timestamp` (the end date of each 7-day period) and `joinday` (individual days within each window)

### Final aggregation
- **Combining components**: Join the rolling window structure with direct chat activities on matching dates to create a comprehensive view of chat activity within each 7-day period
- **Chat counting**: Count distinct `chat_id` values within each 7-day rolling window to determine unique active direct chats
- **Team adjustment**: Apply floor division by 2 to the distinct chat count as advised by the OpenChat team, likely accounting for the bilateral nature of direct conversations where each chat involves two participants
- **Dimensional breakdowns**: Calculate the final metric across three dimensional breakdowns: project level (no additional filtering), product level (grouped by `product_id`), and chain level (grouped by `chain_id`)
- **Final calculation**: The weekly active direct chats equals the floor of (distinct chat IDs with activity in rolling 7-day window) divided by 2, calculated separately for each dimensional breakdown

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

Unique active direct chats over a rolling 7-day window.

OpenChatCHAT

Active direct chats (weekly)

Active direct chats (weekly)

Competitive landscape