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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website

Social

OpenChat

# OpenChat Active direct chats (daily) Methodology

## 1. Overview

The "active direct chats daily" metric measures the daily count of unique direct message conversations that had activity on the OpenChat social platform. This metric tracks engagement in one-on-one conversations by counting distinct chat sessions that had user interactions (messages, edits, reactions, or tips) on a given day. The final count is divided by 2 as a temporary adjustment advised by the OpenChat team, likely to account for the bidirectional nature of direct chats where both participants are counted.

## 2. Data sources

- **OpenChat application events**: Native event logs from the OpenChat social platform running on the Internet Computer blockchain
- **User interaction data**: Records of user activities including message sending, message editing, reaction additions, and message tipping
- **Chat metadata**: JSON payload data containing chat identifiers and chat type classifications (direct vs other chat types)
- **Event system logs**: Timestamped records of all user actions within the OpenChat application

## 3. Step-by-step calculation

### Component A: Event deduplication and filtering
- **Purpose**: Clean and filter raw OpenChat events to focus on chat activity
- **Logic**: Start with raw OpenChat events from the Internet Computer blockchain. Deduplicate events based on timestamp, user identifier, event name, source, and JSON payload content using row numbering to keep only the first occurrence of identical events. Filter the deduplicated events to include only chat activity events: `message_sent`, `message_edited`, `reaction_added`, and `message_tipped`
- **Key fields**: `timestamp`, `name` (event type), `user`, `source`, `payload` (JSON containing chat details)

### Component B: Chat metadata extraction and classification
- **Purpose**: Extract chat information and standardize the data structure
- **Logic**: Parse the JSON payload to extract `chat_id` and `chat_type` fields. Truncate timestamps to daily granularity for aggregation. Add standard metadata including market sector ('social'), business segment ('social'), and chain identifier ('internetcomputer'). The `chat_type` field is crucial for distinguishing between direct chats and other types of conversations
- **Key fields**: `chat_id` (unique chat identifier), `chat_type` (direct vs other), daily timestamp, standardized metadata fields

### Component C: Direct chat filtering
- **Purpose**: Isolate direct message conversations from other chat types
- **Logic**: Filter the processed chat activities to include only records where `chat_type` equals 'direct'. This focuses the metric specifically on one-on-one conversations rather than group chats or other communication formats within OpenChat
- **Key fields**: Filtered dataset containing only direct chat activities with all previous fields retained

### Final aggregation
- **Combining components**: Aggregate the filtered direct chat activities by counting distinct `chat_id` values per day
- **Time aggregation**: Group data by daily timestamps to create daily counts
- **Adjustment calculation**: Divide the distinct chat count by 2 as a temporary adjustment recommended by the OpenChat team
- **Dimensional breakdowns**: Create three separate aggregation levels - project-level (all data combined), product-level, and chain-level breakdowns to support different analytical perspectives
- **Schema standardization**: Apply finalization macro to ensure consistent schema structure and fill missing days with zero values to maintain continuous time series
- **Final calculation**: The metric value equals floor(count of distinct chat_ids / 2) for each day, providing the daily count of active direct chat conversations

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

Unique active direct chats over a fixed 24-hour window.

OpenChatCHAT

Active direct chats (daily)

Active direct chats (daily)

Competitive landscape