the ISFC39 Proceedings - International Systemic-Functional ...

2011). In this pilot study, we limited the data to the ones which has the hash tags inbased on the pre-examination of 3,336 tweetsd the ones which are not re-tweets. The hash tags were added 5 in the corpus, conducted by the first-author. The kappaoriginally by senders ofscore [?] is0.88 6 .s for specifying the subject-maters or topics. #save *** were used during the crisis toSC — demand for informing Safety Confirmation of a person or institute e.g. XXXXinformation related with the the Great East Japan Earthquake and Fukushima Daiichini-aru ‘OOOO iin’ shinpai-desu. sensei, kangoshi-san minasan Papers from the 39th ISFCisaster. In total, the corpus contains 7,102 tweets of which size is 179,099 words 2 go-buji de-shoo-ka. ([I]am worrying about OOOO clinic at XXXX. Are the doctors, . nurses, everyone ok?)ID — demand for Informing Damages of buildings or particular areas e.g. shutokendewa,Table Table 1: 1: Hashtags hashtags and yamagataandnumber nothe number of joukyo-wa tweets ‘issai’ hodo-sarete-imasen. yuusendo-kara sure to rikaiwadekimasu-ga, zaikai-kenmin minna fuan desu. hayaku jimoto-no koe-o kikitai desu.of tweets(Around Tokyo, there has been no report about Yamagata prefecture. [I] understandhashtag that the level number of the priority of tweets [of Yamagata], but the people from Yamagata who live#save fukushima around Tokyo are, all, worried. 1,773[I] want to her voices of my home town.)#save PS miyagi — demand for proposing3,158a preventive measures or solution e.g. masukomi kakushasama“hero jisyuku-shite” 1,604 heri-de shuzai-suru to genba no sagyo-in ya kyuukyuu-tai no#save aomori#save iwatekoe-ga kikoe-duraku-naru soo2,115desu. (To the media, “please do not use a helicopter”.Reporting using a helicopter is obstructing the communication among the workers and#save akitarescues at damaged areas.)1,231#save yamagata 1,202SR — demand for supplying (alternative) resources e.g. genjo no monday-wa, houmonkangoshi-san-ga7,102 shiyoo-suru (unique) kuruma no gasoline-fusoku. kongo-wa kibisiku-naru-amo ...Total(The current problem is that the fuel for the cars of home nurses is running out. Theproblem is growing ...)In addition,JV —thedemandtweetsforclassifiedjudging theas requestsvalidity ofwereinformationfurther categorizede.g. jisin tsunamiinto fourno nan-otypesnogaretathe jitaku-hinan-sha requested acts ni or kooky events. no shien-busshi Hereafter, wathey todokanai will to be kiki-mashita. referred as hontoo the types de oflistedbelow based onnalysis1: Identifying solutions. Role shoo ka? of(ITweets heard thatin relief a supplies Crisisby Circumstancegovernments were not delivered to the peoplewhose houses were not damaged by the earthquake or the Tsunami. It is true?urse function of the tweets were analysed for investigating ‘how much part of tweetsrequests’ and ‘howSCmuch— requestpart offortweetsSafetyexpressConfirmation:the informatione.g., I amwhichworriedcontributeabout hospitaltoX at Y. Are the doctors,nurses, everyone ok?e requests’.ID — request for Informing about Damages to buildings or particular areas: e.g., In Tokyo, theretweets in the corpus were classified by three annotators independently into 3 types illushavebeen no report about Yamagata prefecture.Tab 2. The category which at least two of the three annotators 3 I want to hear about my hometown.classified the same ared as the discourse function PS — request of a given for Proposing tweet. Thea kappa Solution: score e.g., of To this the annotation media, “please task do[?]not use helicopters”. Theirnoise is interfering with the communication among rescue workers.SR — request for Supplying (alternative) Resources: e.g., Home nurseries are running out of petrol.The situation is getting worse ...The result of Analysis 3 is presented in Fig ??. SC occupies over 60% of the Requests, followed by PSber of words was counted These (13.1%), withcategories SR MeCab (12.2%) 0.98and were andID UniDic (11.1%). established 1.3.12. JV Space is only based and 0.4% signs on despite the such Acar as pre-examination full and stop Muraki’s were [2]claim of 3,336 that unreliable tweetsd in the number. Somere-tweets containon Twittermorewasthantheonebiggesttagconducted by the first author. Multiple problem coding the users was have allowed faced during when the one disaster. tweet The contained few occurrence morethem are authors.thanoftwoJVtypes.may beThea resultkappaof excludingscore (Daviesthe re-tweets& Fleiss,from the1992) corpuswas may0.88stem 26 ,fromwhichthesuggestsfact that ourthatcorpustheis composed of the tweets of which hash tags are limited to those of Tohoku (North-East) area. There mayinter-agreement was almost perfect. The categories were decided by majority vote of thebe difference in the type 3 of requests according to the area. For instance, the type of requests would bethree different annotators between (none Tokyo of and whom Fukushima. were authors). As a result of the annotation, over 60% of theThe relationship between the result of Analysis 2 and 3 is illustrated in Tab ??.requests were categorized as SC, followed by PS (13.1%), SR (12.2%), and ID (11.1%).3.5 Analysing Requests with Appraisal theoryinternalised externalised no ALSC 170* 209** 979**ID 6 13 46PS 8 67** 44**SR 10 72** 48**5 the tweets include the Request, Information Offer and Others6 The kappa score was calculated based on 1,681 tweets which were not double coded.Figure 1: Proportion of tweets based on types of solutions3.1 Analysis of Requests from Tweets Analyzing Directness: Congruentor Incongruent?To analyze the directness of the requests, we classified the tweets based on congruence.The notion of congruence is employed as a criterion for determining a request’s directness.According to Halliday (1999), a congruent pattern between a lexico-grammar and semanticsis the one that “evolved” while an incongruent pattern refers to any cross-coupling within this26 The kappa score was calculated based on the 1,681 tweets that were not multiple coded.5137