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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Social

OpenChat

# OpenChat Message count Methodology

## 1. Overview

The OpenChat message count metric measures the daily volume of distinct messages sent on the OpenChat decentralized messaging platform. This metric quantifies user engagement and activity levels by counting individual message sending events, providing insights into the platform's daily usage patterns and communication volume. The calculation aggregates message sending events from the Internet Computer blockchain where OpenChat operates, grouping by day and various message attributes to create comprehensive daily message count statistics.

## 2. Data sources

The calculation relies on the following data sources:
- **OpenChat platform event logs**: Raw event data from the OpenChat decentralized messaging application running on the Internet Computer blockchain
- **Event payload data**: Structured JSON data within events containing message metadata such as chat identifiers, message types, sender information, and message characteristics
- **User interaction data**: User activity records capturing message sending events with associated timestamps and user addresses
- **Chat metadata**: Information about chat types, chat IDs, message threads, and sender bot status embedded in event payloads

## 3. Step-by-step calculation

### Component A: Event deduplication
- **Purpose**: Remove duplicate event records from the raw OpenChat event stream to ensure accurate message counting
- **Logic**: Raw events from OpenChat platform logs are deduplicated using a window function that partitions by `timestamp`, `user`, event `name`, `source`, and the JSON string representation of the `payload`. A row number is assigned within each partition, and only the first occurrence of each unique event combination is retained. This prevents double-counting of messages that may appear multiple times in the raw event stream due to data ingestion processes or platform-level event republishing.
- **Key fields**: `timestamp` (event occurrence time), `name` (event type), `user` (sender address), `source` (data source identifier), `payload` (JSON message metadata)

### Component B: Message event filtering and extraction
- **Purpose**: Isolate message sending events and extract relevant message metadata from the event payload
- **Logic**: From the deduplicated events, only events with `name` equal to `message_sent` are selected, as these represent actual message sending activities. The JSON `payload` field is parsed to extract key message attributes including `chat_id` (unique chat identifier), `chat_type` (type of chat such as direct message or group), `length` (message character count), `message_type` (format of message content), `sender_is_bot` (boolean indicating automated sender), and `thread` (thread identifier for threaded conversations). The `user` field is renamed to `user_address` for consistency.
- **Key fields**: `chat_id`, `chat_type`, `length`, `message_type`, `sender_is_bot`, `thread`, `user_address`

### Component C: Daily message aggregation
- **Purpose**: Group messages by day and dimensional attributes, then count the total number of messages in each group
- **Logic**: Message events are grouped by truncated daily timestamps and various dimensional attributes including `source`, `user_address`, `chat_id`, `chat_type`, `length`, `message_type`, `sender_is_bot`, and `thread`. Static values are assigned for classification: `market_sector_id` and `business_segment_id` are both set to `social` to categorize OpenChat as a social platform, `chain_id` is set to `internetcomputer` to identify the blockchain, and `product_id` is set to null. The `timestamp_grain` is set to `day` to indicate daily aggregation. Within each group combination, the number of messages is calculated using a count aggregation.
- **Key fields**: Daily truncated `timestamp`, `message_count` (COUNT of messages per group)

### Final aggregation
- **Combining components**: The grouped message counts flow through a finalization macro that standardizes the schema, validates identifier fields, and ensures temporal completeness
- **Gap filling**: Missing days in the time series are filled with zero values using a `fill_zeros` strategy to maintain continuous daily metrics
- **Schema validation**: The finalization process applies standard metric schema formatting and validates that all required identifier fields are properly populated
- **Chain filtering**: Final results are filtered based on Internet Computer blockchain availability to ensure data quality and consistency with chain operational periods
- **Final calculation**: The final metric value represents the count of distinct messages sent per day, segmented by user, chat, and message characteristics, providing comprehensive daily message volume statistics for the OpenChat platform

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

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