Summary of your 'study carrel' ============================== This is a summary of your Distant Reader 'study carrel'. The Distant Reader harvested & cached your content into a collection/corpus. It then applied sets of natural language processing and text mining against the collection. The results of this process was reduced to a database file -- a 'study carrel'. The study carrel can then be queried, thus bringing light specific characteristics for your collection. These characteristics can help you summarize the collection as well as enumerate things you might want to investigate more closely. This report is a terse narrative report, and when processing is complete you will be linked to a more complete narrative report. Eric Lease Morgan Number of items in the collection; 'How big is my corpus?' ---------------------------------------------------------- 204 Average length of all items measured in words; "More or less, how big is each item?" ------------------------------------------------------------------------------------ 6146 Average readability score of all items (0 = difficult; 100 = easy) ------------------------------------------------------------------ 47 Top 50 statistically significant keywords; "What is my collection about?" ------------------------------------------------------------------------- Top 50 lemmatized nouns; "What is discussed?" --------------------------------------------- 11655 pandemic 5802 influenza 5433 virus 4184 health 3945 % 2746 study 2606 time 2581 disease 2575 case 2463 patient 2407 risk 2403 response 2216 infection 2023 datum 1912 country 1788 impact 1681 effect 1619 model 1615 people 1611 population 1579 outbreak 1546 research 1544 level 1490 vaccine 1472 analysis 1440 care 1430 system 1426 rate 1370 change 1347 number 1303 measure 1275 group 1248 individual 1229 result 1185 crisis 1165 child 1161 information 1160 epidemic 1143 year 1139 strategy 1105 death 1079 factor 1077 community 1071 day 1064 policy 1053 life 1035 government 1004 hospital 999 term 978 intervention Top 50 proper nouns; "What are the names of persons or places?" -------------------------------------------------------------- 3559 COVID-19 2307 ⁄ 1488 al 1411 H1N1 1247 et 935 Health 911 . 779 China 703 SARS 694 Pandemic 663 Influenza 573 H5N1 555 United 502 • 461 States 419 World 412 uenza 392 UK 391 HA 375 A 368 US 342 NA 342 Coronavirus 329 CoV-2 316 CDC 296 Table 293 April 290 New 282 March 261 Kong 258 Hong 257 National 243 Organization 240 Ebola 238 India 230 USA 226 Europe 226 Disease 219 U.S. 218 C 216 PCR 210 WHO 198 Public 197 Italy 191 Covid-19 190 J 185 Africa 181 May 179 H3N2 178 Research Top 50 personal pronouns nouns; "To whom are things referred?" ------------------------------------------------------------- 4209 it 4072 we 2179 they 666 them 603 i 418 us 240 themselves 228 you 224 one 153 itself 124 he 82 me 47 ourselves 45 she 19 him 17 yourself 17 oneself 13 myself 12 himself 10 's 8 her 3 s 3 herself 2 em 1 thyself 1 theirs 1 t202 1 ours 1 mrnas 1 mg 1 his 1 covid-19 1 aspx&searchtype=&id=55420001 1 a-172 Top 50 lemmatized verbs; "What do things do?" --------------------------------------------- 39253 be 9630 have 2663 use 1926 include 1831 do 1574 provide 1400 increase 1369 base 1331 show 1207 report 1187 make 1081 need 1080 take 1037 relate 1016 develop 964 see 949 reduce 926 consider 912 associate 904 follow 901 require 894 give 878 identify 870 affect 838 find 809 lead 788 become 759 cause 731 suggest 718 occur 698 compare 661 emerge 658 work 639 help 631 infect 588 result 555 allow 551 know 533 understand 521 estimate 520 indicate 514 remain 509 change 505 perceive 494 continue 490 create 487 assess 481 address 475 present 464 perform Top 50 lemmatized adjectives and adverbs; "How are things described?" --------------------------------------------------------------------- 4543 not 2760 more 2724 also 2421 - 2357 such 2186 other 2157 high 2135 social 1833 public 1703 well 1397 most 1326 global 1311 however 1309 human 1286 only 1277 new 1275 covid-19 1248 many 1223 first 1219 economic 1211 different 1130 low 1080 as 1067 early 1050 clinical 983 pandemic 954 likely 946 important 913 medical 910 long 900 severe 890 e.g. 817 large 800 even 794 significant 784 specific 768 viral 749 mental 715 available 710 respiratory 709 seasonal 708 current 701 national 668 effective 660 possible 659 general 656 less 654 infectious 627 non 623 very Top 50 lemmatized superlative adjectives; "How are things described to the extreme?" ------------------------------------------------------------------------- 505 most 254 good 193 least 160 high 139 Most 85 large 81 great 81 bad 40 low 37 late 29 early 24 big 15 ⁄ 13 strong 12 young 12 small 11 long 8 deadly 8 common 7 poor 7 near 7 close 6 old 6 fast 5 weak 5 easy 4 tough 4 simple 4 new 4 mild 4 Least 3 short 2 strict 2 stark 2 slight 2 sick 2 quick 2 fit 2 deep 2 broad 2 -which 1 warm 1 supergu 1 steep 1 staunch 1 southernmost 1 smooth 1 slow 1 sharp 1 severe Top 50 lemmatized superlative adverbs; "How do things do to the extreme?" ------------------------------------------------------------------------ 892 most 119 least 28 well 16 hard 4 worst 2 highest 1 near 1 lowest 1 long 1 fast 1 -particularly Top 50 Internet domains; "What Webbed places are alluded to in this corpus?" ---------------------------------------------------------------------------- Top 50 URLs; "What is hyperlinked from this corpus?" ---------------------------------------------------- Top 50 email addresses; "Who are you gonna call?" ------------------------------------------------- 1 ssd05@ic.ac.uk 1 rochwerg@mcmaster.ca 1 mosconia1@gmail.com 1 dr.ivanrubinic@gmail.com.how 1 benedicto.crespo.sspa@juntadeandalucia.es 1 keogh-brown@lshtm.ac.uk Top 50 positive assertions; "What sentences are in the shape of noun-verb-noun?" ------------------------------------------------------------------------------- 18 pandemic is not 14 pandemic is over 13 pandemic has also 12 pandemic is likely 12 vaccine is available 9 % had moderate 9 pandemic is still 8 analyses are almost 8 models are very 8 virus was not 7 % did not 7 influenza is not 7 pandemic has already 7 pandemic has not 7 study did not 7 virus was first 6 data are not 6 pandemic are likely 6 pandemic are more 6 pandemic did not 6 research did not 6 study has several 6 viruses do not 5 countries are generally 5 models are most 5 pandemic are not 5 pandemic has dramatically 5 pandemic is different 5 patients do not 5 people are more 5 study does not 5 vaccine becomes available 5 viruses were able 4 cases is ideal 4 countries do not 4 data are available 4 data is often 4 data needs models 4 diseases was very 4 effects are not 4 impact is quite 4 model are still 4 models are appropriate 4 models are much 4 models are useful 4 models become relevant 4 models is much 4 pandemic does not 4 pandemic is far 4 pandemic using household Top 50 negative assertions; "What sentences are in the shape of noun-verb-no|not-noun?" --------------------------------------------------------------------------------------- 4 vaccines are not available 3 % reported no change 3 data are not available 3 pandemic has not only 2 cases are not due 2 population was not susceptible 2 systems has not yet 2 virus are not fully 1 % having no acute 1 % is not negligible 1 % reporting no loss 1 % took no action 1 analyses were not possible 1 analysis is not necessarily 1 analysis was not possible 1 care including not only 1 care is no surprise 1 care was not negatively 1 cases are not true 1 cases had no risk 1 country has not yet 1 country is not so 1 covid-19 does not necessarily 1 covid-19 is not unexpected 1 covid-19 was not available 1 covid-19 were not uniquely 1 data are no more 1 data are not yet 1 data have not yet 1 data was not symmetrically 1 data were not available 1 disease are not yet 1 disease was not previously 1 diseases was not merely 1 effects are not consistent 1 effects are not easily 1 effects are not usually 1 health are not altogether 1 infection have not always 1 infection have not well 1 infection is not completely 1 infection is not normally 1 infection were not public 1 infections affect not only 1 infections are not limited 1 influenza is not as 1 influenza is not clearly 1 influenza is not ordinarily 1 model did not adequately 1 models are not yet A rudimentary bibliography --------------------------