Twitter is both nicer and smarter on the weekends. It's like going back in time. Hypothesis: the sort of people who start and join Twitter mobs use Twitter mainly to procrastinate at work.
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Replying to @paulg
Paul, indeed I can confirm your thesis correct. There are a number of sentiment indicators I use that track this. The http://hedonometer.org is one and unless there are overt events the sine wave is positive on most weekends. Other indicators are even more pronounced.
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Replying to @BrianRoemmele
That's very interesting. I'd love to see a graph with just 7 points representing the average for each day.
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Replying to @paulg
Paul, great question. I built a protocol that presents +512 to -512 on any given day. This is a meta protocol that is elicited from sources like the Hednometer. With Hednometer this is a ~6 month, we see the red and pink points (weekend) showing mass events that draw high Affect:pic.twitter.com/YTXcb70KwT
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Replying to @BrianRoemmele @paulg
Paul, one artifact of my protocol is knowledge and insights one can draw. For example, I have advised startups not to announce anything on days that are in the -300 to -512 range, for we can correlate rather high daily noise to signal in any message they wish to communicate.
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Replying to @BrianRoemmele @paulg
Are you familiar with the Global Coherence Initiative Random Variance Data https://www.heartmath.org/gci/gcms/live-data/global-consciousness-project/ … What I have found is this supposedly "random" data has quantifiable cyclical characteristics that make its fluctuations forecastable 65-70% of the time.
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Indeed, I use the EGG as part of my protocols. It is highly accurate if used correctly as a predictor and precursor. This can be achieved locally with a IIED or world wide as EGG. Most folks glaze over on this stuff until it is in a product and useful. This is what I shall do.
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Replying to @BrianRoemmele @paulg
Exactly. For instance, I have found that the incidents of mass shootings correlate with forecasted extremes over 70% of the time. I have found other clear markers of behavior too for relationships, job performance, optimal team composition, etc.
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