Count Mentions Per User - Solution & Explanation

Problem Overview: You receive a sequence of events where users can mention other users. The task is to compute how many times each user gets mentioned. The main challenge is that events must be processed in the correct chronological order and different event types can change how mentions are counted.

Approach 1: Direct Simulation (Naive) (Time: O(n * m), Space: O(m))

The most straightforward strategy is to iterate through the events and simulate the system exactly as described. For each event, parse the mention information and update a counter array or hash map for the mentioned users. If an event references multiple users, iterate through each referenced ID and increment their counters. This works but becomes inefficient when mention rules require repeatedly checking many users (for example broadcasting to all users or evaluating conditions across the entire user list). Because each event can trigger operations over many users, the worst‑case runtime grows to O(n * m) where n is the number of events and m is the number of users.

Approach 2: Sorting + Simulation (Time: O(n log n), Space: O(n + m))

A better approach sorts all events by their timestamp first. This guarantees that mentions are processed in the exact order they occur. After sorting, iterate through the events once and simulate the system state. Maintain a data structure such as an array or hash map to store the mention count for each user. When processing an event, parse the mention tokens and update counts immediately. Sorting ensures that earlier actions always affect later ones correctly, which avoids complicated backtracking logic.

This approach relies on a combination of sorting and step‑by‑step simulation. Each event is processed once after sorting, so the dominant cost is the sort itself: O(n log n). Mention updates are constant time using array indexing or hash lookups, which keeps the simulation efficient even when many users exist. Storing counts and event data requires O(n + m) space.

The key insight is that chronological ordering removes ambiguity. Once events are sorted, the problem becomes a deterministic scan of the event list. This pattern appears frequently in problems combining array processing with time‑based state updates.

Recommended for interviews: The sorting + simulation approach is the expected solution. Interviewers want to see that you first enforce chronological order and then maintain a simple state while scanning the events. Explaining the naive simulation briefly shows you understand the baseline, but implementing the sorted simulation demonstrates stronger algorithmic judgment and cleaner complexity.