Referring as a Collaborative Process

Improving disaster management and recovery techniques is one of national priorities given the huge toll caused by man-made and nature calamities. Data-driven disaster management aims at applying advanced data collection and analysis technologies to achieve more effective and responsive disaster management, and has undergone considerable progress in the last decade. However, to the best of our knowledge, there is currently no work that both summarizes recent progress and suggests future directions for this emerging research area. To remedy this situation, we provide a systematic treatment of the recent developments in data-driven disaster management. Specifically, we first present a general overview of the requirements and system architectures of disaster management systems and then summarize state-of-the-art data-driven techniques that have been applied on improving situation awareness as well as in addressing users’ information needs in disaster management. We also discuss and categorize general data-mining and machine-learning techniques in disaster management. Finally, we recommend several research directions for further investigations.

https://dl.acm.org/doi/pdf/10.1145/3017678

Data-Driven Techniques in Disaster Information Management

Internet and social media have become a widespread, large scale and easy to use platform for real-time information dissemination. It has become an open stage for discussion, ideology expression, knowledge dissemination, emotions and sentiment sharing. This platform is gaining tremendous attraction and a huge user base from all sections and age groups of society. The matter of concern is that up to what extent the contents that are circulating among all these platforms every second changing the mindset, perceptions and lives of billions of people are verified, authenticated and up to the standards. This paper puts forward a holistic view of how the information is being weaponized to fulfil the malicious motives and forcefully making a biased user perception about a person, event or firm. Further, a taxonomy is provided for the classification of malicious information content at different stages and prevalent technologies to cope up with this issue form origin, propagation, detection and containment stages. We also put forward a research gap and possible future research directions so that the web information content could be more reliable and safer to use for decision making as well as for knowledge sharing.

Fake news, rumor, information pollution in social media and web: A contemporary survey of state-of-the-arts, challenges and opportunities

Communicating on Twitter during a disaster

A typology of Twitter use before, during, and after Typhoon Haiyan was tested.Time of use, location, type of user, and social media engagement were tested.Retweet information, coordinate relief, and memorialize victims were the main uses.Recommendations for future research and applications in crises are presented. Social media in crisis situations, such as natural disasters, have been recognized by scholars and practitioners as key communication channels that can complement traditional channels. However, there is limited empirical examination from the user perspective of the functions that social media play and the factors that explain such uses. In this study we examine Twitter use during and after Typhoon Haiyan pummeled the Philippines. We tested a typology of Twitter use based on previous research, and explored external factors - time of use and geographic location - and internal factors - type of stakeholders (e.g. ordinary citizens, journalists, etc.) and social media engagement - to predict these uses. The results showed that different stakeholders used social media mostly for dissemination of second-hand information, in coordinating relief efforts, and in memorializing those affected. Recommendations for future research and applications in future crises are also presented.

Communicating on Twitter During a Disaster: An Analysis of Tweets During Typhoon Haiyan

After the Great East Japan Earthquake in Japan 2011, numerous tweets were exchanged on Twitter. Several studies have already pointed out that micro-blogging systems have shown potential advantages in emergency situations, but it remains unclear how people use them. This paper presents a case study of how people used Twitter after the Great East Japan Earthquake. First, we gathered tweets immediately after the earthquake and analyzed various factors, including locations. The results revealed two findings: (1) people in the disaster area tend to directly communicate with each other (reply-based tweet). On the other hand,(2) people in the other area prefer spread the information from the disaster area by using Re-tweet.

Use trend analysis of twitter after the great east japan earthquake

Recently, medical records are increasingly written on electronic media instead of on paper, thereby increasing the importance of information processing in medical fields. We have organized an NTCIR-10 pilot task for medical records. Our pilot task, MedNLP, comprises three tasks: (1) de-identification, (2) complaint and diagnosis, and (3) free. These tasks represent elemental technologies used to develop computational systems supporting widely diverse medical services. Development has yielded 22 systems for task (1), 15 systems for task (2), and 1 system for task (3). This report presents results of these systems, with discussion clarifying the issues to be resolved in medical NLP fields.

/pdf/overview-of-the-ntcir-10-mednlp-task-2wvmhyzo1b.pdf

Overview of the NTCIR-10 MedNLP Task

Little is known about the relationship between mild cognitive impairment (MCI) and changes to language abilities. Here, we used the revised Hasegawa Dementia Scale (HDS-R) to identify suspected MCI in elderly individuals. We then analyzed written and spoken narratives to compare the language abilities between study participants with and without MCI in order to explore the relationship between cognitive and language abilities, and to identify a possible indicator for the early detection of MCI and dementia. We recruited 22 people aged 74 to 86 years (mean: 78.32 years; standard deviation: 3.36). The participants were requested to write and talk about one of the happiest events in their lives. Based on HDS-R scores, we divided the participants into 2 groups: the MCI Group comprised 8 participants with a score of 26 or lower, while the Healthy Group comprised 14 participants with a score of 27 or higher. The transcriptions of both written and spoken samples for each participant were used in the measurement of NLP-based language ability scores. Our analysis showed no significant differences in writing abilities between the 2 groups in any of the language ability scores. However, analysis of the spoken narrative showed that the MCI Group had a significantly larger vocabulary size. In addition, analysis of a metric that signified the gap in content between the spoken and written narratives also revealed a larger vocabulary size in the MCI Group. Individuals with early-stage MCI may be engaging in behavior to conceal their deteriorating cognition, thereby leading to a temporary increase in their active spoken vocabulary. These results indicate the possible detection of early stages of reduced cognition before dementia onset through the analysis of spoken narratives.

/pdf/vocabulary-size-in-speech-may-be-an-early-indicator-of-4qu2ifeibg.pdf

Vocabulary Size in Speech May Be an Early Indicator of Cognitive Impairment

Purpose – This aim of this paper is to elucidate rumor propagation on microblogs and to assess a system for collecting rumor information to prevent rumor-spreading. Design/methodology/approach – We present a case study of how rumors spread on Twitter during a recent disaster situation, the Great East Japan earthquake of March 11, 2011, based on comparison to a normal situation. We specifically examine rumor disaffirmation because automatic rumor extraction is difficult. Extracting rumor-disaffirmation is easier than extracting the rumors themselves. We classify tweets in disaster situations, analyze tweets in disaster situations based on users' impressions and compare the spread of rumor tweets in a disaster situation to that in a normal situation. Findings – The analysis results showed the following characteristics of rumors in a disaster situation. The information transmission is 74.9 per cent, representing the greatest number of tweets in our data set. Rumor tweets give users strong behavioral facilita...

How do rumors spread during a crisis?: Analysis of rumor expansion and disaffirmation on Twitter after 3.11 in Japan

Translation repair plays an important role in intercultural communication that involves machine translation. It can be used to create messages that have very few translation mistakes. The accuracy of the results of translation repair when an original sentence is rewritten has not yet been evaluated. The improvement brought about by translation repair has to be demonstrated in order to apply translation repair to multilingual communication. Therefore, we have evaluated the translation repair of Japanese-English, Japanese-Chinese, and Japanese-Korean translations using back translation. We have used test sentences with a character count ranging from 15 to 32. On the basis of these evaluation experiments, we have estimated the accuracy and the cost of translation repair. (1) After nearly six rounds of translation repair work in three languages, the average translation accuracy of the sentences used in the experiment was improved (the meaning of the translated sentences was almost the same as that of the original sentences). In the experiment, 65% of the sentences were improved to the level of highly accurate. Moreover, 99% of the sentences were improved to the level of moderately accurate. 2) The cost of repairing a sentence depended on the number of translation-difficult words or phrases that were contained in the sentence. When the quality of a translation was low, finding the word or phrase to be modified was a difficult task. Thus, the cost corresponded to the quality of the translation.

Mai Miyabe

Papers

Use trend analysis of twitter after the great east japan earthquake

Overview of the NTCIR-10 MedNLP Task

Vocabulary Size in Speech May Be an Early Indicator of Cognitive Impairment

How do rumors spread during a crisis?: Analysis of rumor expansion and disaffirmation on Twitter after 3.11 in Japan

Effects of undertaking translation repair using back translation