It will surprise nobody that I like data. By extension, it should surprise nobody that what I like about blogging is getting instant feedback on whether people found a post interesting and relevant or not. This is in contrast to writing a journal article, where you will wait minimally a year or two before anyone starts citing you (if they ever do).
Sometimes the results are surprising. I expected my posts on the suspicious data underlying recent graduate school rankings to make a splash, but the two posts together got a grand total of 2 comments and 16 tweets (some of which are automatically generated by FieldofScience). I didn't expect posts on my recent findings regarding pronoun processing to generate that much interest, but they got 6 comments and 26 tweets, putting them among the most popular, at least as far as Twitter is concerned.
To get a sense of which topics you, dear readers, find the most interesting, I compiled the statistics from all my posts from the fall semester and tabulated those data according to the posts' tags. Tags are imperfect, as they reflect only how I decided to categorize the post, but they're a good starting point.
Here are the results, sorted by average number of retweets:
Since we all know correlation = causation, if I want to make a really popular post, I should label it "findings, publication, peer review". If I want to ensure it is ignored, I shouldn't give it a label at all.
At this point, I'd like to turn it over the crowd. Are these the posts you want to see? If not, what do you want to read more about? Or if you think about your favorite blogs, what topics do you enjoy seeing on those blogs?
How I feel about data.
Sometimes the results are surprising. I expected my posts on the suspicious data underlying recent graduate school rankings to make a splash, but the two posts together got a grand total of 2 comments and 16 tweets (some of which are automatically generated by FieldofScience). I didn't expect posts on my recent findings regarding pronoun processing to generate that much interest, but they got 6 comments and 26 tweets, putting them among the most popular, at least as far as Twitter is concerned.
To get a sense of which topics you, dear readers, find the most interesting, I compiled the statistics from all my posts from the fall semester and tabulated those data according to the posts' tags. Tags are imperfect, as they reflect only how I decided to categorize the post, but they're a good starting point.
Here are the results, sorted by average number of retweets:
| label | #Posts... | Tweets_Ave... | Reddit_Ave... | Comments_Ave... |
| findings | 2 | 13 | 0 | 3 |
| publication | 3 | 13 | 5 | 5 |
| peer review | 4 | 12 | 13 | 10 |
| universal grammar | 5 | 10 | 2 | 8 |
| pronouns | 3 | 10 | 0 | 2 |
| GamesWithWords.org | 2 | 9 | 0 | 1 |
| scientific methods | 7 | 8 | 7 | 7 |
| neuroscience | 1 | 8 | 0 | 5 |
| overheard | 1 | 7 | 0 | 1 |
| language development | 2 | 7 | 0 | 7 |
| Web-based research | 6 | 7 | 0 | 1 |
| science and society | 3 | 6 | 1 | 6 |
| language | 6 | 6 | 1 | 3 |
| education | 2 | 6 | 0 | 1 |
| journalism | 2 | 6 | 18 | 9 |
| politics | 7 | 6 | 0 | 2 |
| science blogging | 2 | 6 | 1 | 2 |
| language acquisition | 1 | 5 | 0 | 0 |
| recession | 2 | 5 | 1 | 3 |
| the future | 1 | 5 | 0 | 0 |
| vision | 1 | 5 | 0 | 1 |
| graduate school | 4 | 5 | 0 | 3 |
| science in the media | 3 | 5 | 12 | 7 |
| method maven | 2 | 5 | 18 | 10 |
| media | 3 | 4 | 0 | 1 |
| psychology career path | 1 | 4 | 0 | 2 |
| lab notebook | 3 | 3 | 0 | 1 |
| none | 4 | 3 | 0 | 0 |
Since we all know correlation = causation, if I want to make a really popular post, I should label it "findings, publication, peer review". If I want to ensure it is ignored, I shouldn't give it a label at all.
At this point, I'd like to turn it over the crowd. Are these the posts you want to see? If not, what do you want to read more about? Or if you think about your favorite blogs, what topics do you enjoy seeing on those blogs?
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