How do I track the growth of specific keywords on Reddit over time?

Reddit keyword growth tracking is best done with a combination of historical data collection, automated data extraction, and time-series visualization. Use a lightweight data pipeline to pull mentions over time, store results, and chart trends to see how interest shifts.

Overview of approach

• Identify target keywords or phrases.
• Gather time-stamped mention data from Reddit (posts and comments).
• Normalize data to a consistent time granularity (daily or weekly).
• Visualize trends and compute growth metrics (percentage change, CAGR).
• Compare multiple keywords side by side.

Essential tools and data sources

• <strong>Reddit API (official)</strong>: Access posts and comments, apply rate limits, get metadata.
• <strong>Pushshift API</strong>: Historic Reddit data with easier access for large ranges and time filters.
• <strong>Web scraping (sparingly)</strong>: For niche subreddits or non-text fields, ensure compliance with terms.
• <strong>Data storage</strong>: CSV/JSON files or a lightweight database (SQLite) for small projects.
• <strong>Analytics/Visualization</strong>: Python (pandas, matplotlib), Google Sheets, or BI tools.

Step-by-step setup (practical workflow)

1. <strong>Define keywords clearly</strong>: Include variations and common misspellings.
2. <strong>Choose a time window</strong>: E.g., last 12 months, last 5 years.
3. <strong>Fetch data</strong>:

• Use Pushshift to pull posts and comments containing each keyword within the time window.
• Save fields: id, author, created_utc, subreddit, body/title, keywords matched.

1. <strong>Normalize data</strong>:

• Convert timestamps to date.
• Count mentions per day/week per keyword.

1. <strong>Store results</strong>:

• Create a table with columns: date, keyword, mentions.

1. <strong>Compute growth metrics</strong>:

• Daily/weekly totals.
• Growth rate = (current period - previous period) / previous period.
• Optional: moving averages (7-day, 14-day) to smooth spikes.

1. <strong>Visualize</strong>:

• Line charts with one line per keyword.
• Include shaded regions for noise (rolling variance).

1. <strong>Validate</strong>:

• Cross-check with a sample of posts to confirm matches.
• Watch for data gaps due to API limits.

1. <strong>Interpret results</strong>:

• Identify rising topics, seasonal patterns, or events driving spikes.
• Compare keywords to find relative popularity.

Practical example workflow (mini-project)

• Target keywords: "AI", "machine learning", "deep learning".
• Timeframe: last 6 months.
• Data steps:
• Query Pushshift for each keyword, date-bounded.
• Save results to a CSV with date and count per day.
• Load CSV in Python, group by date, and compute daily counts.
• Visualization steps:
• Plot a multi-line chart with dates on the x-axis and mentions on the y-axis.
• Add a 14-day moving average line for each keyword.
• Interpretation:
• Note spikes around product launches or conferences.
• Assess which topics grow faster year over year.

Data quality considerations and pitfalls

• <strong>API rate limits</strong>: Respect limits; batch requests and implement retries.
• <strong>Overlap in keywords</strong>: Use exact phrase matching or negative filters to avoid double counting.
• <strong>Noise vs. signal</strong>: Reddit volume can spike due to memes or bots; use smoothing.
• <strong>Subreddit bias</strong>: Some topics are subreddit-specific; consider weighting by subreddit size.
• <strong>Time alignment</strong>: Ensure all keywords are aggregated by the same time unit.

Recommendations for accuracy and efficiency

• Start with a small pilot: 2–3 keywords, 1–3 months.
• Use moving averages to reduce daily volatility.
• Store raw data separately from analyzed results for auditability.
• Schedule regular updates (e.g., weekly) to track growth over time.
• Document your workflow so you can reproduce results later.

Quick tips and examples

• Example metric: 14-day growth rate for each keyword.
• Compare AI-related keywords against a baseline like “discussion” or “news” to gauge relative momentum.
• Use subreddits with broad reach (e.g., r/artificial, r/MachineLearning) for more stable signals, but also check niche communities for niche signals.
• Filter out promotional posts if necessary to focus on organic interest.

Common mistakes to avoid

• Using a single data source; combine Pushshift with Reddit API for coverage.
• Ignoring time zones; convert UTC to local-equivalent bins if needed.
• Averaging counts without accounting for post/comment deletion or edits.
• Forgetting to normalize for overall Reddit activity changes over time.

Best practices checklist

• [ ] Define clear keywords and variations.
• [ ] Decide time granularity and window.
• [ ] Use a robust data collection method with retries.
• [ ] Normalize dates and counts consistently.
• [ ] Apply smoothing and growth calculations.
• [ ] Visualize with clear legends and labels.
• [ ] Validate a sample of data manually.
• [ ] Document process and store source data.

SEE ALSO:

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• Which software helps in generating reddit wiki content
• How do i automate the discovery of new niche subreddits