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Twitter-Based Study Figures Out Saddest Spots In New York City
Nerval's Lobster writes "A new research paper from the New England Complex Systems Institute, titled "Sentiment in New York City" (PDF), attempts to pull off something that would have been impossible—or at least mind-bogglingly difficult and time-consuming—before the invention of online social networks: figure out the block-by-block happiness level of the biggest metropolis in the United States. In order to generate their 'sentiment map' of New York City, the researchers analyzed data from 603,954 Tweets (collected via Twitter's API) organized by census block. 'This method, combined with geotagging provided by users, enables us to gauge public sentiment on extremely fine-grained spatial and temporal scales,' read the paper's abstract. The study took emoticons and word choice into account when deciding whether particular Tweets were positive or negative in sentiment. According to that flood of geotagged Tweets, people are happiest near New York City's public parks, and unhappiest near transportation hubs. Happiness increased closer to Times Square, the declined around Penn Station, the Port Authority, and the entrance to the Midtown Tunnel. People were in a better mood at night and on weekends, and more negative about the world between the hours of 9 A.M. and 12 P.M. None of this is surprising: who wouldn't be happy amidst the greenery of a public park, or borderline-suicidal while stuck in traffic or waiting for a late train? The correlation between happiness and Times Square is almost certainly due to that neighborhood's massive influx of tourists, all of them Tweeting about their vacation. But as with previous public-sentiment studies, using Twitter as a primary data source also introduces some methodology issues: for example, a flood of happy Tweets from tourists could disguise a more subdued and longstanding misery among a neighborhood's residents, many of whom probably aren't tweeting every thirty seconds about a Broadway show or the quality of Guy Fieri's food."
The saddest parts of New York City are not where people who own mobile devices and laptops convene. The saddest parts of New York City are where people are wearing trash bags, begging for food and shelter... They are not begging for attention by Tweeting their pretentious frivolity.
This assumes that everyone uses Twitter.
It's funny, isn't it? People who are heavy users of Twitter are absolutely convinced that EVERYONE is on Twitter. Because in their tiny world, it's true. It's like when some World of Warcraft nerd starts spewing jargon in front of everyone and nobody knows what's going on...alts, tank, twitchy, whatever.
What's really frightening is how data like this is being taken seriously. Stock markets move based on Twitter.
Shutting down free speech with violence isn't fighting fascism. It IS fascism!
Does that guy really think he's tearing Fieri a new one by incessantly asking nothing but sarcastic questions for two pages of review? Seriously, learn some English writing and criticism techniques if you're going to be a critic for a living.
You were critically hit for no damage. The bruise will look nice, and maybe the scars will make good party talk.
The saddest parts of New York City are not where people who own mobile devices and laptops convene. The saddest parts of New York City are where people are wearing trash bags, begging for food and shelter... They are not begging for attention by Tweeting their pretentious frivolity.
True. But I wonder how long will it be before the researchers apply the same techniques to analyse block by block the political beliefs of the residents. It may even be hyper accurate if you assume quote reasonably that :-
1. those who tweet about their political beliefs tend to be more passionate about it and are more likely to vote; and
2. those who don't are apathetic to politics and are less likely to vote.
If they can gauge something as subjective as 'happiness', gauging something more definite like the voting predisposition of the residents of an area would appear to be a simpler task.
NJ.