J. Med. Internet Res. - HealthTrust: a social network approach for retrieving online health videos.

Tópicos

{ health(1844) social(1437) communiti(874) }
{ search(2224) databas(1162) retriev(909) }
{ motion(1329) object(1292) video(1091) }
{ patient(2315) diseas(1263) diabet(1191) }
{ perform(999) metric(946) measur(919) }
{ studi(1119) effect(1106) posit(819) }
{ extract(1171) text(1153) clinic(932) }
{ method(1557) propos(1049) approach(1037) }
{ research(1085) discuss(1038) issu(1018) }
{ visual(1396) interact(850) tool(830) }
{ activ(1138) subject(705) human(624) }
{ can(774) often(719) complex(702) }
{ system(1976) rule(880) can(841) }
{ howev(809) still(633) remain(590) }
{ studi(1410) differ(1259) use(1210) }
{ high(1669) rate(1365) level(1280) }
{ take(945) account(800) differ(722) }
{ care(1570) inform(1187) nurs(1089) }
{ perform(1367) use(1326) method(1137) }
{ record(1888) medic(1808) patient(1693) }
{ cost(1906) reduc(1198) effect(832) }
{ use(976) code(926) identifi(902) }
{ estim(2440) model(1874) function(577) }
{ detect(2391) sensit(1101) algorithm(908) }
{ data(1737) use(1416) pattern(1282) }
{ inform(2794) health(2639) internet(1427) }
{ imag(1057) registr(996) error(939) }
{ bind(1733) structur(1185) ligand(1036) }
{ featur(3375) classif(2383) classifi(1994) }
{ network(2748) neural(1063) input(814) }
{ treatment(1704) effect(941) patient(846) }
{ error(1145) method(1030) estim(1020) }
{ chang(1828) time(1643) increas(1301) }
{ algorithm(1844) comput(1787) effici(935) }
{ data(1714) softwar(1251) tool(1186) }
{ data(3963) clinic(1234) research(1004) }
{ blood(1257) pressur(1144) flow(957) }
{ spatial(1525) area(1432) region(1030) }
{ patient(2837) hospit(1953) medic(668) }
{ data(2317) use(1299) case(1017) }
{ group(2977) signific(1463) compar(1072) }
{ first(2504) two(1366) second(1323) }
{ time(1939) patient(1703) rate(768) }
{ analysi(2126) use(1163) compon(1037) }
{ structur(1116) can(940) graph(676) }
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{ method(2212) result(1239) propos(1039) }
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{ method(1219) similar(1157) match(930) }
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{ model(2220) cell(1177) simul(1124) }
{ general(901) number(790) one(736) }
{ method(984) reconstruct(947) comput(926) }
{ featur(1941) imag(1645) propos(1176) }
{ case(1353) use(1143) diagnosi(1136) }
{ risk(3053) factor(974) diseas(938) }
{ system(1050) medic(1026) inform(1018) }
{ import(1318) role(1303) understand(862) }
{ model(2341) predict(2261) use(1141) }
{ compound(1573) activ(1297) structur(1058) }
{ health(3367) inform(1360) care(1135) }
{ model(3480) simul(1196) paramet(876) }
{ monitor(1329) mobil(1314) devic(1160) }
{ ehr(2073) health(1662) electron(1139) }
{ state(1844) use(1261) util(961) }
{ research(1218) medic(880) student(794) }
{ model(2656) set(1616) predict(1553) }
{ age(1611) year(1155) adult(843) }
{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
{ sampl(1606) size(1419) use(1276) }
{ gene(2352) biolog(1181) express(1162) }
{ data(3008) multipl(1320) sourc(1022) }
{ intervent(3218) particip(2042) group(1664) }
{ patient(1821) servic(1111) care(1106) }
{ use(2086) technolog(871) perceiv(783) }
{ can(981) present(881) function(850) }
{ cancer(2502) breast(956) screen(824) }
{ use(1733) differ(960) four(931) }
{ drug(1928) target(777) effect(648) }
{ result(1111) use(1088) new(759) }
{ implement(1333) system(1263) develop(1122) }
{ survey(1388) particip(1329) question(1065) }
{ decis(3086) make(1611) patient(1517) }
{ activ(1452) weight(1219) physic(1104) }
{ method(1969) cluster(1462) data(1082) }

Resumo

CKGROUND: Social media are becoming mainstream in the health domain. Despite the large volume of accurate and trustworthy health information available on social media platforms, finding good-quality health information can be difficult. Misleading health information can often be popular (eg, antivaccination videos) and therefore highly rated by general search engines. We believe that community wisdom about the quality of health information can be harnessed to help create tools for retrieving good-quality social media content.OBJECTIVES: To explore approaches for extracting metrics about authoritativeness in online health communities and how these metrics positively correlate with the quality of the content.METHODS: We designed a metric, called HealthTrust, that estimates the trustworthiness of social media content (eg, blog posts or videos) in a health community. The HealthTrust metric calculates reputation in an online health community based on link analysis. We used the metric to retrieve YouTube videos and channels about diabetes. In two different experiments, health consumers provided 427 ratings of 17 videos and professionals gave 162 ratings of 23 videos. In addition, two professionals reviewed 30 diabetes channels.RESULTS: HealthTrust may be used for retrieving online videos on diabetes, since it performed better than YouTube Search in most cases. Overall, of 20 potential channels, HealthTrust's filtering allowed only 3 bad channels (15%) versus 8 (40%) on the YouTube list. Misleading and graphic videos (eg, featuring amputations) were more commonly found by YouTube Search than by searches based on HealthTrust. However, some videos from trusted sources had low HealthTrust scores, mostly from general health content providers, and therefore not highly connected in the diabetes community. When comparing video ratings from our reviewers, we found that HealthTrust achieved a positive and statistically significant correlation with professionals (Pearson r10 = .65, P = .02) and a trend toward significance with health consumers (r7 = .65, P = .06) with videos on hemoglobinA(1c), but it did not perform as well with diabetic foot videos.CONCLUSIONS: The trust-based metric HealthTrust showed promising results when used to retrieve diabetes content from YouTube. Our research indicates that social network analysis may be used to identify trustworthy social media in health communities.

Resumo Limpo

ckground social media becom mainstream health domain despit larg volum accur trustworthi health inform avail social media platform find goodqual health inform can difficult mislead health inform can often popular eg antivaccin video therefor high rate general search engin believ communiti wisdom qualiti health inform can har help creat tool retriev goodqual social media contentobject explor approach extract metric authorit onlin health communiti metric posit correl qualiti contentmethod design metric call healthtrust estim trustworthi social media content eg blog post video health communiti healthtrust metric calcul reput onlin health communiti base link analysi use metric retriev youtub video channel diabet two differ experi health consum provid rate video profession gave rate video addit two profession review diabet channelsresult healthtrust may use retriev onlin video diabet sinc perform better youtub search case overal potenti channel healthtrust filter allow bad channel versus youtub list mislead graphic video eg featur amput common found youtub search search base healthtrust howev video trust sourc low healthtrust score most general health content provid therefor high connect diabet communiti compar video rate review found healthtrust achiev posit statist signific correl profession pearson r p trend toward signific health consum r p video hemoglobinac perform well diabet foot videosconclus trustbas metric healthtrust show promis result use retriev diabet content youtub research indic social network analysi may use identifi trustworthi social media health communiti

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