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 (daily) Methodology

## 1. Overview

Active channels daily measures the number of unique OpenChat channels that had at least one user engagement activity (message, edit, reaction, or tip) on a given day. This metric tracks channel-level activity and community engagement within the OpenChat social platform on Internet Computer blockchain. It answers the business question: "How many distinct channels are actively being used for communication each day?"

## 2. Data sources

- **OpenChat events data**: Native event logs from OpenChat's social platform containing timestamped user activities, event types, user identifiers, and JSON payloads with chat metadata
- **Internet Computer blockchain data**: Underlying blockchain infrastructure data supporting the OpenChat platform
- **Chat activity event streams**: Real-time streams of user engagement events including messages, edits, reactions, and tips
- **Event payload data**: JSON-structured metadata containing chat identifiers, chat types, and interaction details

## 3. Step-by-step calculation

### Component A: Event deduplication
- **Purpose**: Clean and deduplicate raw OpenChat event data to ensure accurate counting
- **Logic**: Raw OpenChat events are deduplicated by partitioning on `timestamp`, `user`, event `name`, `source`, and JSON payload content. A row number is assigned to each partition and only the first occurrence is kept. This handles potential duplicate event submissions or data ingestion issues.
- **Key fields**: `timestamp`, `name` (event type), `user` (user identifier), `source`, `payload` (JSON metadata)

### Component B: Engagement event filtering  
- **Purpose**: Identify and filter for user engagement activities that indicate active channel usage
- **Logic**: From the deduplicated events, select only engagement-related event types: `message_sent`, `message_edited`, `reaction_added`, and `message_tipped`. These represent actual user interactions that demonstrate channel activity rather than passive events.
- **Key fields**: `name` field filtered for specific engagement event types, `payload` containing chat metadata

### Component C: Chat activity processing
- **Purpose**: Extract chat metadata and standardize temporal granularity for daily aggregation
- **Logic**: Parse the JSON `payload` to extract `chat_id` and `chat_type` fields. Truncate timestamps to daily boundaries using day-level truncation. Add standard classification fields including `market_sector_id` and `business_segment_id` set to 'social', and `chain_id` set to 'internetcomputer'.
- **Key fields**: `chat_id` (unique channel identifier), `chat_type` (channel vs direct message), `timestamp` (truncated to daily grain)

### Component D: Channel filtering
- **Purpose**: Focus specifically on channels rather than all chat types (excludes direct messages)
- **Logic**: Filter the chat activities to include only records where `chat_type` equals 'channel'. This ensures the metric captures public/group channel activity and excludes private direct message conversations.
- **Key fields**: `chat_type` filtered to 'channel' value only

### Final aggregation
- **Combining components**: Create three dimensional breakdowns of the channel activity data: project-level aggregation (no dimensional splits), product-level aggregation (grouped by `product_id`), and chain-level aggregation (grouped by `chain_id`). Each breakdown counts distinct `chat_id` values per day.
- **Currency conversion**: No currency conversion required as this is a count-based metric
- **Time aggregation**: Daily boundaries are established through timestamp truncation to day level, with `timestamp_grain` set to 'day'
- **Final calculation**: Count distinct `chat_id` values per day for each dimensional breakdown, union all dimensional views together, apply the `finalize_metric_active_channels_daily` macro with zero-fill strategy to handle missing dates, and apply chain availability filtering for data quality assurance

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

Unique active channels over a fixed 24-hour window.

OpenChatCHAT

Active channels (daily)

Active channels (daily)

Competitive landscape