J. Med. Internet Res. - The 1% rule in four digital health social networks: an observational study.

Tópicos

{ health(1844) social(1437) communiti(874) }
{ system(1976) rule(880) can(841) }
{ state(1844) use(1261) util(961) }
{ network(2748) neural(1063) input(814) }
{ inform(2794) health(2639) internet(1427) }
{ studi(1119) effect(1106) posit(819) }
{ treatment(1704) effect(941) patient(846) }
{ data(1714) softwar(1251) tool(1186) }
{ case(1353) use(1143) diagnosi(1136) }
{ howev(809) still(633) remain(590) }
{ group(2977) signific(1463) compar(1072) }
{ activ(1452) weight(1219) physic(1104) }
{ framework(1458) process(801) describ(734) }
{ chang(1828) time(1643) increas(1301) }
{ drug(1928) target(777) effect(648) }
{ survey(1388) particip(1329) question(1065) }
{ clinic(1479) use(1117) guidelin(835) }
{ extract(1171) text(1153) clinic(932) }
{ studi(1410) differ(1259) use(1210) }
{ ehr(2073) health(1662) electron(1139) }
{ gene(2352) biolog(1181) express(1162) }
{ activ(1138) subject(705) human(624) }
{ use(1733) differ(960) four(931) }
{ method(2212) result(1239) propos(1039) }
{ imag(1057) registr(996) error(939) }
{ method(1219) similar(1157) match(930) }
{ featur(3375) classif(2383) classifi(1994) }
{ patient(2315) diseas(1263) diabet(1191) }
{ take(945) account(800) differ(722) }
{ problem(2511) optim(1539) algorithm(950) }
{ error(1145) method(1030) estim(1020) }
{ algorithm(1844) comput(1787) effici(935) }
{ import(1318) role(1303) understand(862) }
{ compound(1573) activ(1297) structur(1058) }
{ perform(1367) use(1326) method(1137) }
{ health(3367) inform(1360) care(1135) }
{ model(2656) set(1616) predict(1553) }
{ cost(1906) reduc(1198) effect(832) }
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{ method(984) reconstruct(947) comput(926) }
{ search(2224) databas(1162) retriev(909) }
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{ research(1085) discuss(1038) issu(1018) }
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{ spatial(1525) area(1432) region(1030) }
{ record(1888) medic(1808) patient(1693) }
{ model(3480) simul(1196) paramet(876) }
{ monitor(1329) mobil(1314) devic(1160) }
{ research(1218) medic(880) student(794) }
{ patient(2837) hospit(1953) medic(668) }
{ data(2317) use(1299) case(1017) }
{ age(1611) year(1155) adult(843) }
{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
{ sampl(1606) size(1419) use(1276) }
{ data(3008) multipl(1320) sourc(1022) }
{ first(2504) two(1366) second(1323) }
{ intervent(3218) particip(2042) group(1664) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
{ use(2086) technolog(871) perceiv(783) }
{ can(981) present(881) function(850) }
{ analysi(2126) use(1163) compon(1037) }
{ structur(1116) can(940) graph(676) }
{ high(1669) rate(1365) level(1280) }
{ cancer(2502) breast(956) screen(824) }
{ use(976) code(926) identifi(902) }
{ result(1111) use(1088) new(759) }
{ estim(2440) model(1874) function(577) }
{ decis(3086) make(1611) patient(1517) }
{ method(1969) cluster(1462) data(1082) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

CKGROUND: In recent years, cyberculture has informally reported a phenomenon named the 1% rule, or 90-9-1 principle, which seeks to explain participatory patterns and network effects within Internet communities. The rule states that 90% of actors observe and do not participate, 9% contribute sparingly, and 1% of actors create the vast majority of new content. This 90%, 9%, and 1% are also known as Lurkers, Contributors, and Superusers, respectively. To date, very little empirical research has been conducted to verify the 1% rule.OBJECTIVE: The 1% rule is widely accepted in digital marketing. Our goal was to determine if the 1% rule applies to moderated Digital Health Social Networks (DHSNs) designed to facilitate behavior change.METHODS: To help gain insight into participatory patterns, descriptive data were extracted from four long-standing DHSNs: the AlcoholHelpCenter, DepressionCenter, PanicCenter, and StopSmokingCenter sites.RESULTS: During the study period, 63,990 actors created 578,349 posts. Less than 25% of actors made one or more posts. The applicability of the 1% rule was confirmed as Lurkers, Contributors, and Superusers accounted for a weighted average of 1.3% (n=4668), 24.0% (n=88,732), and 74.7% (n=276,034) of content.CONCLUSIONS: The 1% rule was consistent across the four DHSNs. As social network sustainability requires fresh content and timely interactions, these results are important for organizations actively promoting and managing Internet communities. Superusers generate the vast majority of traffic and create value, so their recruitment and retention is imperative for long-term success. Although Lurkers may benefit from observing interactions between Superusers and Contributors, they generate limited or no network value. The results of this study indicate that DHSNs may be optimized to produce network effects, positive externalities, and bandwagon effects. Further research in the development and expansion of DHSNs is required.

Resumo Limpo

ckground recent year cybercultur inform report phenomenon name rule principl seek explain participatori pattern network effect within internet communiti rule state actor observ particip contribut spare actor creat vast major new content also known lurker contributor superus respect date littl empir research conduct verifi ruleobject rule wide accept digit market goal determin rule appli moder digit health social network dhsns design facilit behavior changemethod help gain insight participatori pattern descript data extract four longstand dhsns alcoholhelpcent depressioncent paniccent stopsmokingcent sitesresult studi period actor creat post less actor made one post applic rule confirm lurker contributor superus account weight averag n n n contentconclus rule consist across four dhsns social network sustain requir fresh content time interact result import organ activ promot manag internet communiti superus generat vast major traffic creat valu recruit retent imper longterm success although lurker may benefit observ interact superus contributor generat limit network valu result studi indic dhsns may optim produc network effect posit extern bandwagon effect research develop expans dhsns requir

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