I analysed 20 years of my chats

A researcher has analyzed 20 years of personal chat data from multiple social media and messaging platforms, uncovering patterns and challenges in extracting meaningful insights from digital communication history.

The individual collected and parsed message archives from platforms including VK, Twitter, Facebook, Instagram, and Telegram, covering periods from 2000s to 2020s. They filtered out noise—such as media, links, emojis, and filler words—and classified the remaining content into categories like life events, banter, and emotional cues.

The analysis involved handling complex issues like nickname variations, language differences, and encryption limitations, especially with platforms like Facebook and Instagram. Despite these challenges, the researcher identified a vocabulary plateau since 2008, with most new words appearing early in their life. TIL that 32 bit time will run out in 2038, while 64 bit time will run out approximately 292 billion years from now

Why It Matters

This work demonstrates how long-term digital communication can be systematically analyzed to reveal personal patterns, social dynamics, and emotional states. Amazon Web Services – Four Years and Out It also highlights the technical challenges of working with noisy, multi-platform data, and the potential for personal data to inform self-understanding or improve relationship management.

Background

The project builds on the idea that digital footprints, accumulated over decades, can serve as a proxy for life experiences and emotional health. It follows a broader trend of self-tracking and personal data analysis, with roots in earlier efforts like Tim Urban’s ‘Your Life in Weeks’ and personal journaling practices. The analysis also reflects ongoing concerns about data privacy, platform limitations, and the complexity of human language across contexts.

“Most of my vocabulary was locked in my early 20s, and the novelty rate has declined since 2008, plateauing at 6%.”

— the researcher

“Handling nicknames and platform-specific IDs is a major challenge in mapping social interactions across multiple services.”

— the researcher

What Remains Unclear

It remains unclear how accurately the analysis can capture emotional states or life events solely from filtered text data, given the complexity of language and context. The interpretation of social patterns and the reliability of automated classification methods are still under development.

What’s Next

The researcher plans to refine classification algorithms, incorporate more contextual data, and explore how this analysis can inform personal growth or relationship management. Future work may include developing tools for ongoing life tracking and emotional analysis.

Key Questions

What methods were used to filter noise from the chat data?

The researcher sampled messages, manually reviewed common short tokens, and used frequency analysis to identify and filter filler words, links, media, and emojis, reducing the data to roughly 52,000 unique lemmas.

How did the researcher handle variations in nicknames and platform differences?

He used heuristics and Named Entity Recognition (NER) models but found them insufficient. Instead, he relied on custom heuristics, morphological analysis, and manual sampling to map different nicknames and account for platform-specific IDs.

Can this analysis accurately reflect emotional or life events?

While the filtering and classification efforts aim to identify patterns related to life events and emotions, the researcher notes that interpretation is complex and some nuances may be lost or misrepresented due to data noise and language variability.

Source: Hacker News