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Have you ever wondered if the Internet is having a good day, if it is happy or sad, or maybe feeling a little depressed? Well, probably not, but thanks to a team of information access researchers from Amsterdam University you might be able to find out.

work by tapping into the mood level flags set by more than 10 million LiveJournal bloggers. By scanning the 150,000 or so daily postings that include one of the 132 different available mood flags, the MoodViews software collects the information and then looks for trends, Although there is a temptation to write this off as some kind of digital biorhythm, there is no doubting that emotional trends have emerged that make sense. So the Internet tends to be drunk at the weekend, feel loved on Valentine’s Day, was worried during Hurricane Katrina and positively scared after the 7/7 London bombings last year. The researchers do not see it as just a bit of fun however, but rather hope it will help them to develop new methods for searching, discovering and retrieving blogs.

You can keep an eye on how the Internet is feeling at any given time by checking out the MoodGrapher which updates and plots those mood levels every 10 minutes by showing the actual mood reported and the rate of change within any given hour. Then there’s the MoodTeller which uses natural language processing together with machine learning to guesstimate mood levels from the text of blog entries rather than those mood flags. Next on the launch agenda is MoodSpotter, due any time now, which will reveal those moods typically associated with any topic.

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This thread describes an early experiment that tried to read collective emotion from public online posts. That kind of work can reveal useful short-term patterns inside a specific community, but it’s not a universal thermometer for “how the Internet feels.” The value comes from careful sampling, transparent methods, and honest reporting of uncertainty.

Common pitfalls and practical checks:

  • Sampling bias: the people who post are not a random sample. Weight or segment results by known demographics when possible.
  • Signal type: explicit self-reported tags and inferred sentiment from text are different signals — treat them separately and compare.
  • Language and context: sarcasm, idioms, negation and multilingual content break simple word-count methods. Validate with human annotations.
  • Bots, repeats and spam: filter automated or duplicate content before aggregating.
  • Temporal issues: day-of-week cycles and event-driven spikes require baseline windows and smoothing to avoid overinterpreting noise.
  • Ethics and privacy: aggregate and anonymize; follow platform terms of service and local privacy rules.

A short workflow to get meaningful, repeatable results:

  1. define the precise question and population;
  2. collect data with permissions and provenance;
  3. clean (dedupe, bot-filter, language-detect);
  4. build a small annotated gold set (few hundred examples) and use it to evaluate any model;
  5. choose a combined approach (lexicon checks + supervised model + simple time-series smoothing);
  6. report aggregates with confidence intervals and clear caveats about who the signal covers.

Replying to points in the thread: ’s curiosity is exactly the right start; is correct that an observational system isn’t automatically a predictor; and ’s stereotype about posters is a reminder not to generalize individual behavior to an entire platform. A small labeled pilot (≈500 items) will quickly show whether the signal is usable before anyone claims broad conclusions.

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Member Avatar for goldeagle2005

So now we're supposed to anticipate the Internet's behavior? That's rich..

Member Avatar for mikeandike22

I dont trust people who have livejournal anyone who has to resort to online postings about themselves probably is a bit emo.

Member Avatar for happygeek

<goldeagle2005> Not anticipate, observe. There's no compulsion either, other than the fact that it does become a little addicitive :)

<mikeandike22> The vast majority of people who blog are, ultimately, blogging about themselves: their opinion, their ideas, their feelings. LiveJournal just happens to provide a methodology to measure the collective mood of those posters.

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