hana

Key:

  • degere o ka pilikia; oluolu oluolu , meakino meakino , ikaika paakiki , Nui nui
  • pono makemakika ( pono makemakika )
  • pono coding ( pono coding )
  • ikepili haul ( ikepili ohi )
  • koʻu punahele ( koʻu punahele )

  1. [ meakino , koʻu punahele ] Algorithmic hoopalaimaka hoi he pilikia me Google Flu kū i ke au. E heluhelu i ka pepa e Lazer et al. (2014) , ae kakau iho i ka pokole, ike mail i ka engineer ma Google wehewehe i ka pilikia a me ka mohai i ka manaʻo o ka mea hiki ke koho i ka pilikia.

  2. [ meakino ] Bollen, Mao, and Zeng (2011) kuleana iʻikepili mai Twitter hiki ke hoʻohana 'ia e kilokilo i ke kumukuai kuai. Keia mea i hoʻoholo 'alakai aku i ka haku o ka pa a puu dala-Derwent Capital aha kanaka-e ana ma ke kumukuai makeke ma muli o kaʻikepili mai Twitter (Jordan 2010) . Heaha ka hoike e ia oukou makemake e ike mua o kau ko oukou kala iloko o ia dala?

  3. [ oluolu ] Oiai kekahi lehulehu ola Uwao ka huahekili e-ka pakalōlō e like me ka 'kōkua no ka uiki cessation, kekahi e ao e pili ana i ka Loaʻaʻia ka pilikia, e like me ka mea kiʻekiʻe-pae o ka nicotine. Manao wale ana i ke kanaka noiʻi hoʻoholoʻia e noiʻi i ka lehulehu manao ma e-ka pakalōlō, ma kaʻohiʻohi e-ka pakalōlō-pili Twitter pou, a e mālama nei e hoʻolaha Ka Ikepili.

    1. He aha ka hiki biases ekolu a ua nui kou manaʻo loa e pili ana ma keia mahele?
    2. Clark et al. (2016) wale ia ka mahele holo. Mua, lakou ohi 850,000 Tweets i hoʻohana e-uahi paka-nā hua'ōlelo mai January 2012 ma o Dekemaba 2014. Ma ka pilina nana, uaʻike ihola nui o keia mau Tweets, ua automated ia ( 'o ia hoʻi, aole i hua mai ma kānaka) a me ka nui o keia mau automated Tweets heʻano nui kālepa o ka. Lakou nui ka hōʻike 'ka loaʻaʻana Algorithm e kaawale automated Tweets mai ke aloha Tweets. E ho ohana i kēia He huai ae Algorithm loaa ia lakou ka 80% o ka Tweets ua automated. No anei keia i hoʻoholo 'ia e hoʻololi i kou pane i ka hapa (a)?
    3. I ka wa a lakou i hoʻohālike au i ka olelo la, ma ke aloha a me ka automated Tweets ike aku la lakou i ka automated Tweets ua oi aku maikaʻi ma mua o ke aloha Tweets (6.17 versus 5.84). No anei keia i hoʻoholo 'ia e hoʻololi i kou pane aku (b)?

  4. [ oluolu ] In Nowemapa 2009, Twitter hoololi i ka ninau i loko o ka Tweet pahu mai "Heaha ka oukou hana?" I, "He aha Ka hanaia?" (Https://blog.twitter.com/2009/whats-happening).

    1. Pehea la ko oukou manao i ka loli ana o na hoʻopili e loli ka poe Tweet a / ai ole ia i ka mea a lakou i Tweet?
    2. Name hookahi noiʻi papahana, no ka mea a oukou e makemake i ke ake nui, "Heaha ka oukou hana?" E wehewehe aha.
    3. Name hookahi noiʻi papahana, no ka mea a oukou e makemake i ke ake nui "Heaha Ka hanaia?" E wehewehe aha.

  5. [ meakino ] Kwak et al. (2010) ua kālailai 41.7 miliona hoʻohana piliʻike, 1.47 ieeeea? Nohona pili, 4262 trending kumuhana apau loa, a he 106 miliona Tweets ma waena o June 6th, a me Iune 31st, 2009. Ma muli o kēia Ka Ikepili lakou i manao ai, Twitter lawelawe 'ana aku e like me ka hou meakino o ka' ike kaʻana like ma mua o ka nohona i ulanaʻia.

    1. E manao Kwak ¶ AL hoʻoholo ', heaha' ano o ka hana noiʻi e oukou e hana aku me Twitter ikepili? He aha 'ano o ka hana noiʻi e ia oukou, aole e hana me Twitter ikepili? No ke aha mai?
    2. I ka makahiki 2010, Twitter hou i ka wai e hahai hana ana kā kamaʻilio i ka mea hoʻohana. Ekolu manaʻo paipai, ua ike ia i ka manawa ma ka papa kuhikuhiEʻaoʻao. pinepine manaʻo paipai ua e holo ana mai kekahi ka "makamaka-o-hoaaloha," a hoi ae hoʻokaʻaʻike ua hookiekieiaʻe ia i loko o ka manaʻo paipai. Mea hoʻohana ke kiʻi hou i ka ike i ka hou lākou o nā manaʻo paipai a hoopai i ka palapala, me ka papa inoa hou o na koi. Aole anei oukou manao i keia mea hou kēia mau mea kanu i kou pane i ka hapa o ka hoʻololi)? No ke aha la paha ke aha ole?
    3. Su, Sharma, and Goel (2016) ana kūpono i ke kanawai o Owai la ka mea e hahai hana, a loaa ia oiai mea hoʻohana ma o ka hiʻona pae pomaikai mai na koi, kūpono kou oi aku mamua o awelika o ka loa Popular mea hoʻohana pomaikai no. No anei keia i hoʻoholo 'ia e hoʻololi i kou pane i ka hapa b)? No ke aha la paha ke aha ole?

  6. [ oluolu ] "Retweets" ua pinepine hoʻohana i ana ole, a hohola ka aoao ma Twitter. Initially, mea hoʻohana i ke kope, ae kāpili i ka Tweet lakou makemake, OAaCO ~ i ka palapala kākau a me kona / kona lawelawe, a me ka hana lima kikokiko i "RT" i mua o ka Tweet e hōʻike 'ana i ia, he retweet. A laila, i ka 2009 Twitter hou i kekahi "retweet" pihi. In June 2016, Twitter hana e hiki ai i nā mea hoʻohana i retweet ko lakou mau Tweets (https://twitter.com/twitter/status/742749353689780224). Anei oukou manao i keia mau mea hou e pili pehea oe ho ohana "retweets" iloko o ko oukou noiʻi? No ke aha la paha ke aha ole?

  7. [ meakino , ikepili ohi , pono coding ] Michel et al. (2011) kūkulu i kekahi kino i waho mai, mai Google ke kākoʻo 'ana o ke digitize puke. E ho ohana i ka mana mua o ke kino, ka mea i paʻiʻia ma ka 2009, a me na luna o 5 miliona digitized puke, nā kākau ua kālailai olelo oAaEeIeIAaIAeO alapine (frequency) e noiʻi ana linguistic hoololi a me ka nohona LIKE. Koke ka Google Books kino lilo i kaulana ike kumu no ka noiʻi, a me ka 2nd hoʻokolohua o ka hōkeoʻikepili, ua hookuu i ka 2012.

    Eia naʻe, Pechenick, Danforth, and Dodds (2015) ia i noiʻi pono e piha i emi iho i ka mea hōʻikeʻikeʻuʻuku kaʻina hana o ke kino ma mua o ka ho ohana ia no ka unuhi i ka laula hopena. Ka papa pukana no ia o ke kino ua hale waihona-me, na kekahi o kela a me keia buke. A? Acoeuoaoa, he kanaka, prolific mea kākau, ua hiki ke noticeably hookomo hou hopuna iloko o ka Google Books lelo. A,ʻepekema nā haʻawina lakou i increasingly substantive hapa o ke kino ma na 1900s. Eia hou, ma ke kapakai a elua wale nō o ka'ōlelo Pelekānia fiction datasets, Pechenick listen AL. loaa hōʻike a pau i hanaia insufficient kānana i ka hana o ka mana mua. ka mea loaʻa a pau o nā ikepili e pono ai no ka hana maʻaneʻi; http://storage.googleapis.com/books/ngrams/books/datasetsv2.html

    1. In Michel listen AL. Ka palapala pepa (2011) , lakou hoʻohana i ka 1st mana o ke English aeaiiuo i, manaʻo i ke alapine (frequency) o oAaEeIeIAaIAeO o na makahiki "1880", "1912" a me "1973", a i ​​manao ai, "kakou poe hoopoina ko makou hala wikiwiki me kela a me keia hele makahiki "(fiku. 3A, Michel listen AL.). Replicate ka'āina me ka 1) 1st hoʻokolohua o ke kino, English dataset (ia e like me ka laau fiku. 3A, Michel listen AL.)
    2. Ano, replicate i ka hookahi kuka pu ka 1st mana, English fiction dataset.
    3. Ano, replicate i ka hookahi kuka pu ka 2nd mana o ke kino, English dataset.
    4. Eia ke oki, replicate i ka hookahi kuka pu ka 2nd mana, English fiction dataset.
    5. E wehewehe i ka oko ao a me na mea like ma waena o kēia mau mea kuka eha. Anei oe i ae mai Michel listen AL. Ka palapala ano o ka malama au? (, Hoʻomaoe: c) a me d) e lilo i ka mea e like me ke Kii 16 ma Pechenick listen'ōlelo).
    6. Ano ia oukou i replicated i keia mea i hoʻoholo 'ia ka hoʻohana okoa Google Books corpora, e koho i kekahi linguistic hoololi a me ka nohona phenomena hōʻike ma Michel listen AL. Ka mua pepa. Anei oe i ae me ko lakou hoomaopopo ana, ma ka malamalama o ka hoʻokau 'hōʻike ma Pechenick listen'ōlelo.? E hanaʻoukou i kekahi manaʻo hoʻopiʻi ikaika, hoao replicate i ka ia pakuhi hoʻohana okoa wale nō o nā aeaiiuo i e like me maluna.

  8. [ nui , ikepili ohi , pono coding , koʻu punahele ] Penney (2016) kaupaona i nā paha ka laha publicity e pili ana NSA / PRISM hoʻomakākiu ( 'o ia hoʻi, ka Snowden hoike ana) i loko o June 2013, ua pili i ka pahikaua a me ka koke emi i kalepa ae la lakou i Wikipedia' atikala ma kumuhana apau loa e ho'āla pilikino pili. Ina pela, i keia loli ma ka hana e mau pahuhopu nei me he ala ia ana mai ka nuipa a hoʻomakākiu. Ke kokoke loa i kahi o Penney (2016) ua i kekahi manawa kapaʻia he 'aʻole i manawa papahana i manao ai, a ua hai mai i na hoʻokokoke mai i loko o ka mokuna e pili ana i approximating hoao mai observational ikepili (Pauku 2.4.3).

    E koho i ke kumuhana hua'ōlelo, Penney haawiia i ka papa hoʻohana ma US Oihana o'āina e malu ai, no ka hoʻokoloʻana a me ka mālamaʻana nohona Media. Ka DHS papa categorizes kekahi mau hua'ōlelo e huli i loko o ka lohe ÿana o kumuhana, oa "Health, ae hoʻomaopopoʻia," "nā pono ka maluhia," a me "Terrorism." No ka mea, i ka hoʻopaʻa 'hui, Penney hoʻohana i nā hua'ōlelo kanahākumamāwalu:ʻo lākou e pili ana i "Terrorism" (nānā Papa 8 i paiiaʻi). He laila aggregated Wikipedia pauku View helu ma ka malama kumu, no ka like kanahākumamāwalu:ʻo lākou Wikipedia 'atikala ma luna o kekahi kanakolu-elua malama manawa, mai ka hoomaka ana mai o Ianuari 2012 a hiki i ka hopena o Augate, 2014. E hooikaika i kona manaʻo hoʻopiʻi,ʻo ia hana no hoi kekahi mau nane pūʻulu, ma ka hoʻokoloʻana 'atikala manao maluna o kekahi kumuhana apau loa.

    Ano,ʻoukou e hele aku e replicate, ae hohola aku Penney (2016) . A pau i ka maka ikepili i oe, e pono no ia ha awina, ua loaʻa mai Wikipedia (https://dumps.wikimedia.org/other/pagecounts-raw/). A i 'ole oe e loaa ia mea, mai ka R OAaEeOOAIE wikipediatrend (Meissner and Team 2016) . I ka wa ia oe palapala, i kou mau manaʻo, e 'oluʻolu e hoailona oukou i aeaiiuo kumu oe i hoʻohana. ( 'Ōlelo Aʻo: keia ia ha awina no hoi i ke Mokuna 6)

    1. Heluhelu mai o Penney (2016) , a replicate Kii 2 e hōʻike ana i ka palapala manao no "Terrorism" -related 'aoʻao ma mua, a ma hope o ka Snowden hoike ana. Ano nā haʻina.
    2. A malaila, replicate laau 4A, ka mea hoʻohālikeʻana i ka hoʻopaʻa 'pūʻulu ( "Terrorism" -related pauku,) me ka comparator hui me hua'ōlelo CATEGORY_NAME ma lalo o "DHS & Other keʻena ma lalo nei" mai ka DHS helu (e nānā i paiiaʻi na Papa 10). Ano nā haʻina.
    3. Ma ka hapa b) oe hoohalikeia ka hoʻopaʻa hui i hookahi comparator hui. no hoi ia i elua kekahi comparator pūʻulu Penney: "aeuiiai ieaia? e malu" -related pauku (i paiiaʻi na Papa 11) a me ka Popular Wikipedia 'aoʻao (i paiiaʻi na Papa 12). E hele mai me ka papa comparator hui, ae ho'āʻo ina ka mea, ua ulu mai ka hapa b) ka 'ikepili i ko oukou koho ana o comparator hui. Ka mea koho o ka comparator hui i nui ke aloha? No ke aha mai?
    4. Ka mea kākau olelo ia hua'ōlelo e pili ana i "Terrorism" ua hoʻohana 'ia e koho i ka Wikipedia waiwai, no ka US aupuni Kuhikuhi' terrorism e like me ka ki hoaponoia no kona online, hoʻomakākiu hana. E like me ke kaha o keia mau 48 "Terrorism" -related hua'ōlelo, Penney (2016) hoi lawelawe 'ia he ana ma luna o MTurk e noi ana respondents e uku kela a me keia o na hua'ōlelo i loko o olelo o ke Aupuni Mai hooluhi mai, Kulekele-ikepili koʻikoʻi, a me Avoidance (i paiiaʻi na Papa 7 a me 8). Replicate i ka anamanaʻo ma MTurk ai ka hoohalike ana i kou mau hualoaʻa.
    5. Ma muli o nā hualoaʻa ma ka hapa d), a me ko oukou heluhelu ana o ka pauku, anei oe i ae i ka inoa koho o ke kumuhana hua'ōlelo i loko o ka mahele hui? No ke aha la paha ke aha ole? Ina ole, ka mea e oe paipai hakahaka?

  9. [ oluolu ] Efrati (2016) hoike, ma muli o anamanaʻo ike, i "huina i kaʻana like '" on Facebook i emi mai ai ma kahi o 5.5% makahiki maluna o keia makahiki, oiai iho "palapala hōʻike i kaʻana like'" he 21% o ka makahiki maluna o keia makahiki. Keia emi, ua nui acute me Facebook mea hoʻohana malalo o 30 makahiki o ka makahiki. Ka hoike aʻela ka emi i ka elua kumu. Hana o ka ulu ana i loko o ka heluʻana o ka "hoaaloha" poe kanaka e ma Facebook. Ka kekahi mea i kekahi i kaʻana like 'ha awina i' oni iki i ka messaging, a me nā kānaka hoʻokūkū e like me Snapchat. Ka hoike no hoi hoikeia na tactics Facebook i ho'āʻo ai e boost kaʻana like 'ia, me ke komo pu News Feed algorithm tweaks mea e kiʻi pou nui koʻikoʻi, e like me periodical hoʻomanaʻo ia o ka palapala pou mea hoʻohana "Ma keia Day" mau makahiki aku nei. He aha implications, ina kekahi, aole ia i keia mau mea, ua ulu i ka noiʻi i makemake e hoʻohana i Facebook e like me ka ikepili kumu?

  10. [ meakino ] Tumasjan et al. (2010) hai mai i ka nui o na Tweets i olelo ia i ka aoao pili aupuni i like me ka nui o na balota i aoao i loaa ma ka German parliamentary koho i ka 2009 (Kii 2.9). Ma kekahi olelo, ua ikea ia oe ke hoʻohana Twitter e kilokilo i ke koho. I ka manawa i keia ao, ua hookaulana ana aku, ua manaoia paʻakikī hoʻopīhoihoi, no ka mea, ua manao e paipai i ka waiwai pono no i na kumu o ka nui ikepili.

    Haawi aku ka maikaʻiʻano a pau i nui ka ikepili, nae, ia oe e koke e-Liloa i kēia hopena. Kelemania ma Twitter i ka 2009, ua hala loa i nā 'elele hui, a me nā kākoʻo o kekahi aoao i Tweet e pili ana i kālai'āina hou pinepine. Pela, he mea kahaha ko oukou naau i na mea a pau i ka hiki biases a pau ia oukou ke manao wale e somehow ana mai. Eia, na hualoaʻa ma Tumasjan et al. (2010) huli aku ai eʻoi aku ka maikaʻi i ka mea oiaio. Iloko o ko lakou mau pepa, Tumasjan et al. (2010) , noonoo iho la eono kālai'āina poe: Karistiano Democrats (CDU), Kalikiano Social Democrats (CSU), SPD, Liberal (KS) (FDP), ma ka lima hema (Die Linke), a me ka Green Party (Grüne). Eia naʻe,ʻo ka mea i ka inoa Kelemānia kālai'āina aoao ma Twitter i kēlā manawa,ʻo ia ka ili Party (Piraten), he aoao e kaua aupuni rula hooponopono o ka Internet. I ka wa o ka ili Party ua komo iloko o ke Ka Ikepili, Twitter Shoes i ka weliweli predictor o ke koho hualoaʻa (Kii 2.9) (Jungherr, Jürgens, and Schoen 2012) .

    Kii 2,9: Twitter Shoes hoike hou aku ai kilokilo i nā hualoaʻa o ka 2009 German koho (Tumasjan listen AL 2010.), Aka, o keia? Acoeueoao huli mai la e hilinai aku ma luna o kekahi ākeʻakeʻa a me ka manaʻo koho (Jungherr, Jürgens, a Schoen 2012).

    Kii 2,9: Twitter Shoes hoike hou aku ai kilokilo i nā hualoaʻa o ka 2009 German koho (Tumasjan et al. 2010) , Aka, o keia? Acoeueoao huli mai la e hilinai aku ma luna o kekahi ākeʻakeʻa a me ka manaʻo koho (Jungherr, Jürgens, and Schoen 2012) .

    A laila, na noiʻi a puni ke ao nei, ua hoʻohana fancier epekema, e like me ka hoʻohana olelo la Ka Ikepili e maopopo ma waena o maikaʻi a me ka io Shoes o na aoao elua, i mea e hoʻoikaika i ka hiki ana o Twitter 'ike e kilokilo i keʻano o nāʻano o nā koho (Gayo-Avello 2013; Jungherr 2015, Ch. 7.) . Eia ko Huberty (2015) manaʻoi hoʻopōkole 'i ka hopena o keia mau hoao ana e kilokilo koho:

    "Na ike forecasting epekema ma muli o ka lawelawe Media, ua nele ae la malalo iho o na koi ana o ka oiaio i mua-nana electoral forecasting. ikea keia mau hoʻolako 'ole' ia e hoʻopiʻi i kumu waiwai o ka lawelawe Media, ma mua i methodological a algorithmic pilikia. O ka pokole, aole ka nohona Media hana, a paha aole e, kaumaha i ka pilina paʻa, pāʻewaʻole, 'elele kiʻi o ka electorate; a me ka pono Eia kekahi laʻana o ka lawelawe Media, nele lawa ikepili e koho i keia mau pilikia Mail hoc. "

    E heluhelu i kekahi o ka noiʻi i ke alakai Huberty (2015) i ka hopena, ae kakau iho i kekahiʻaoʻao Memo i ka moho pili aupuni hōʻike no ka mea, ina a me ka Twitter e hoʻohana i ka ei ke koho ana.

  11. [ meakino ] He aha ka likeʻole o ka sociologist, a me he kakaolelo? E like me Goldthorpe (1991) , ka papa likeʻole o ka sociologist, a me he kakaolelo, he mana hoʻomalu ma luna aeaiiuo ohi. Moʻolelo, ua pio e hoʻohana i laa, ka mea hoi sociologists ke kā ko lakouʻikepili ohi i kekahi mau hana. Heluhelu mai o Goldthorpe (1991) . Pehea i ka likeʻole o sociology a me ka moolelo e pili ana i ka manaʻo o Custommades a me ka Readymades?

  12. [ paakiki ] Kukulu ana i ka ninau mua, Goldthorpe (1991) unuhi i ka nui o ka noho pilikia pane, e like me kekahi mai Nicky dia (1994) i konane Goldthorpe ka wa e kā i aeaiiuo. E möakäka hou i ka Loaʻaʻia hoʻokau 'o ke kā, i aeaiiua, dea hihiu ho'ākāka' ia ma ka Affluent limahana Papahana, he nui anamanaʻo e ana i ka pilina ma waena o ka lawelawe papa, a me ke koho ana i lawelawe 'ia e Goldthorpe, a me nā hoapili ma ka hapalua like 1960s. E like me ka mea i manaʻo mai ka haumana i hoopomaikai nui loa ia papahana ma luna o kaʻikepili loaa aeaiiua, ka Affluent limahana Project ohi 'ikepili i kā no ka hoʻoponopono' i nei i manaoia pähola e pili ana i ka wā e hiki mai ana o ka lawelawe papa i loko o ka au o ka hoʻoulu ola hae. Aka, Goldthorpe a me nā hoapili somehow "poina" e ohi i ka 'ike e pili ana i ke koho ana o na wahine. Eia ko Nicky dia (1994) nā hōʻuluʻulu manaʻo i ka pau Episode:

    ". . . ka mea, ua hiki ole ke pale aku i ka hopena, i na wahine, ua hōʻike 'no ka mea, o keia' kā i 'dataset ua hoʻopaʻaʻia ke paradigmatic kūpili i hoʻokoe' wahine ka hoao ana. Puhiia e ka theoretical hihio o ka papa ike a me ka hana e like me kane preoccupations. . . , Goldthorpe a me kona nā hoapili kūkulu i ke kaʻina o empirical hoike i hanai a hā nai i ko lakou mau theoretical assumptions ma kahi o kaʻilikai ia lakou i ka henua pololei hoao o adequacy. "

    Dea noho iho:

    "O ka empirical hoʻoholo 'ana o ka Affluent limahana Papahana hai mai ia makou e pili ana i ka masculinist aiee o ka hapalua like-kenekulia sociology mamua o lakou i hai aku i ka keʻano o ka hanaʻana o ka stratification, kālai'āina, a me nā ola."

    E hiki anei ia oukou ke manao a pau o na examples kahi kā-i aeaiiuo ohi i na biases o ka 'ike ÿauhau i kūkuluʻia i loko o ia mea? Pehea keia hoohalike i algorithmic hoopalaimaka hoi? He aha implications ke ano o keia mea, no ka wa noiʻi e hoʻohana i Readymades, a me ka wa a lakou e hoʻohana i Custommades?

  13. [ meakino ] Ma keia mokuna, I Akä naÿe no kaʻikepili e noiʻi no ka noiʻi me ka hoʻomalu mooolelo hana e poʻe a me na aupuni. Kekahi kanaka i kapa aku ai i keia mau hoʻomalu mooolelo "loaa aeaiiua," a lakou Akä naÿe me ka " 'ia aeaiiuo." He meaʻoiaʻiʻo a pau i loaa hoʻomalu mooolelo ma ka noiʻi, aka, ua hoi mahalo' ia. No ka mea, laʻana, laekahiʻenehana poʻe pana aku au i alahula mau dala o ka manawa, a me na waiwai e ohi ae curate i ko lakouʻikepili. Pela, ua laua loaa keia mau aaieieno mooolelo, a me 'ia, he pono hilinaʻi nui ma ko oukou kuanaʻike (Kii 2.10).

    Kii 2,10: The kiʻi o ka Duck, a me ka 'Olu; i ka mea a oukou e ike nei hilinaʻi nui maluna o ko oukou mau kuanaʻike. Ke aupuni a me ka hana hoʻomalu moʻolelo i na loaa a 'ia; i ka mea a oukou e ike nei hilinaʻi nui maluna o ko oukou mau kuanaʻike. No ka mea, laʻana, ke kapa ikepili mooolelo ohi ia ma ke kelepona hele poe, ua loaa 'ikepili mai ka kuanaʻike o ka noiʻi. Aka, ua hoʻololi i kēia mau hua'ōlelo ia mooolelo aeaiiuo kuanaʻike o ka mea e hana ana ma ke kaʻina hana Oihana o ke kelepona huakai. Source: Wikimedia maoli

    Kii 2,10: The kiʻi o ka Duck, a me ka 'Olu; i ka mea a oukou e ike nei hilinaʻi nui maluna o ko oukou mau kuanaʻike. Ke aupuni a me ka hana hoʻomalu moʻolelo i na loaa a 'ia; i ka mea a oukou e ike nei hilinaʻi nui maluna o ko oukou mau kuanaʻike. No ka mea, laʻana, ke kapa ikepili mooolelo ohi ia ma ke kelepona hele poe, ua loaa 'ikepili mai ka kuanaʻike o ka noiʻi. Aka, ua hoʻololi i kēia mau hua'ōlelo ia mooolelo aeaiiuo kuanaʻike o ka mea e hana ana ma ke kaʻina hana Oihana o ke kelepona huakai. Source: Wikimedia maoli

    Kūkulu i kumu o ke aeaiiuo kumu kahi e ike aku ia laua e like me ka loaa a 'ia he kōkua i ka wa a ka hoʻohana' ana i ka 'ike kumu no ka noiʻi.

  14. [ oluolu ] Ma ka noʻonoʻo (essay), Karistiano Sandvig a me Eszter Hargittai (2015) wehewehe mai elua ano o ka mīkini noiʻi, kahi a ka mīkini nenoaiu, ua "hana" ai 'ole "mea o ke ao." He kumu hoohalike o ke ano mua o ke kuka nei lakou i kahi Bengtsson, a me nā hoapili (2011) hoʻohana kelepona Paʻi 'ike e Track ka neʻekau ma hope o ka ÷ la i loko o Laua i ka 2010. he laʻana o ka lua o ka ano oia kahi Jensen (2007) haʻawina i ka hoike o Mobile kaul ma Kerala, India hopena i hōʻikeʻia ka functioning o ka makeke no ka iʻa. I loaa i keia kōkua maikaʻi no ka mea, clarifies ia haʻawina hoʻohana mīkini ikepili kumu e loaa pahu hopu o kona kulana okoa a hiki ina lakou e hoohana ana i na mea ano o ka aeaiiuo kumu. I mea e hou aku möakäka hou keia e pono ai, e wehewehe haʻawina eha ia oukou hulina ike ai:ʻelua e ho ohana i ka mīkini ano hana e like me ka mea kani, a me elua e ho ohana i ka mīkini ano hana e like me ka mea o ka mahele. Hiki nō ke hoʻohana examples mai kēia mokuna, ina oukou makemake.

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