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Reader Engagement with Wikipedia’s Medical Content

Maggio, L.; Steinberg, R. M.; Piccardi, T.; Willinsky, J. M.

2019-10-18 scientific communication and education
10.1101/797779 bioRxiv
Show abstract

Wikipedias extensive health and medical entries, maintained by WikiProject Medicine (WPM), are well supported by external links that provide readers with both a means of verifying the sources drawn upon and visiting those sources to learn more about a topic. In analysing how readers approach these links, data was collected on reader engagement with these links on WPM pages and on the rest of Wikipedia over a 32-day period. Readers of WPM pages were found to engage with external links more frequently than readers of the rest of Wikipedia, with WPM readers favoring hovering over a link and footnote clicking, compared to W readers who tended to click more external links per page viewed. Compared to readers of the rest of Wikipedia, WPM readers appear more attentive to the external links function in verifying and authorizing Wikipedia content, than to the educational potential of examining the sources themselves.

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

1
PLOS ONE
● 5266 papers in training set
Top 2%
40.2%
Similar papers in this journal
2
PLOS Digital Health
● 106 papers in training set
Top 0.9%
6.9%
Similar papers in this journal
3
Heliyon
○ 152 papers in training set
Top 0.3%
6.4%
Similar papers in this journal
50% of probability mass above
4
F1000Research
○ 88 papers in training set
Top 0.2%
5.3%
Similar papers in this journal
5
PeerJ
◐ 308 papers in training set
Top 1%
4.1%
Similar papers in this journal
6
npj Digital Medicine
○ 118 papers in training set
Top 1%
4.1%
7
Bioinformatics
◐ 1204 papers in training set
Top 6%
1.9%
8
eLife
● 5828 papers in training set
Top 49%
1.8%
9
Scientific Reports
○ 3612 papers in training set
Top 51%
1.8%
10
Frontiers in Psychology
○ 56 papers in training set
Top 0.7%
1.8%
11
Behavior Research Methods
○ 30 papers in training set
Top 0.3%
1.7%
12
Bioinformatics Advances
◐ 203 papers in training set
Top 3%
1.7%
13
Lab Animal
○ 11 papers in training set
Top 0.1%
1.1%
14
FEBS Open Bio
○ 31 papers in training set
Top 0.5%
1.1%
15
GigaScience
◐ 212 papers in training set
Top 4%
0.9%
16
Royal Society Open Science
● 214 papers in training set
Top 6%
0.9%
17
Open Heart
● 21 papers in training set
Top 0.9%
0.9%
18
eneuro
● 439 papers in training set
Top 8%
0.6%
19
BioData Mining
○ 22 papers in training set
Top 0.8%
0.6%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.