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Location Mention Recognition from
Japanese Disaster-related Tweets

Toshihiro Rokuse[0009-0008-0844-5975] and Osamu Uchida[0000-0003-0211-7504]

Department of Information Media Technology, Tokai University, Hiratsuka, Kanagawa, Japan
trokuse@tokai.ac.jp, o-uchida@tokai.ac.jp

**Abstract.** In order to minimize the damage inflicted by large-scale disasters, it is essential to collect and disseminate information quickly and accurately. In recent years, various national agencies and local municipalities have used Twitter and other highly immediate social media to help focus their disaster relief efforts. Because the volume of information circulating on social media increases rapidly during a disaster, the ability to quickly sort out valuable posts from the massive volume of posts that appear is essential. In the case of Twitter, it is vital for early responders to identify the location of relevant tweets in order to facilitate decision making and focus their response. To help in this task, attempts have been made to use machine learning to classify genres, extract useful information, and identify locations and points of interest for groups of tweets posted during a disaster. However, since preparing training data and building a model during the early stages of a disaster are extremely challenging, using a model built on past disaster tweet data offers a promising possibility. In this study, we focus on three heavy rain disasters that occurred in Japan and examine the extraction of the location mentions in tweets using models learned from tweets posted during prior disasters.

**Keywords:** Disaster, Social media, Location mention, Machine learning.

# 1 Introduction

Prompt and accurate information gathering and dissemination are critical to minimize damage in the event of a large-scale disaster. In recent years, the possibility of using highly immediate and popular social media platforms for this purpose has been the subject of increasing interest. When a disaster strikes, reports of power outages, communication failures, and other damage posted in real-time on such sites can contain information helpful in planning and executing first responses [1]. Several surveys have shown that Twitter, in particular, has been used to transmit, collect, and share information about the damage inflicted by a large-scale disaster [2]. Indeed, there is a growing movement among government agencies and local municipalities which play a key role in times of disaster to gather, disseminate, and act on information available through social media sites like Twitter [3]. However, because the volume of information circulating on social media increases rapidly during a large-scale disaster, there are still issues to be addressed if further utilization of social media is to become part of an effective disaster response strategy. For example, during the earthquake that hit the northern part of Osaka Prefecture on June 18, 2018, it was revealed that at least 270,000 tweets
(including re-tweets) containing the word "earthquake" were posted on Twitter during the 10 minutes following 8:00 a.m., immediately after the main shock was felt [4]. It has also been pointed out that more than 5,200 rescue requests posted on social media were missed during the hurricane that made landfall in Texas, USA, in August 2017 [5]. Given such experiences, there is a clear need to quickly sort out essential information from the vast number of tweets that flood the site. It is also vital for disaster responders to identify the locations noted in the tweets—that is, to extract location mentions—so that appropriate decisions can be made. Table 1 shows examples of tweets posted on Twitter that contain location mentions.

Against this background, efforts have been made to use machine learning to classify genres, extract useful information, and identify locations and points of interest for groups of tweets posted during disasters. However, preparing training data and building models in real-time when a disaster occurs are highly challenging. One of the factors that make the use of machine learning models difficult is data preparation. Since a large number of tweets with a broad range of characteristics circulate in a short period of time during a disaster, it is necessary to select the tweets to be used for learning and to annotate the collected tweets. Such tasks generally require a substantial amount of time. However, from the perspective of minimizing the damage caused by a disaster, it is important to respond quickly.

To reconcile these two conflicting requirements, building a model using tweet data from past disasters offers a promising possibility. Such models are not unprecedented. Suwaileh et al. [6], for example, attempted to extract location mentions from tweets during the 2016 flooding in Houston, Texas, USA, using a model learned from tweets posted during previous disasters. However, to the authors' knowledge, there are no cases in which location mention recognition was used for tweets written in Japanese and posted during disasters in Japan. In this study, we focus on three torrential rain disasters in Japan and examine the extraction of location mentions using a model learned from tweets posted during prior disasters. Specifically, we examine tweets posted during the heavy rain-induced disasters of July 2018, July 2020, and August 2021 (Fig. 1).

One of the main contributions of our paper is the proposed method for constructing a disaster-related dataset from tweets posted in Japanese. Most of the disaster tweet datasets represented in CrisisLex [7] and CrisisNLP [8] are in English, and there appear to be insufficient resources available in other languages, although efforts are underway to build datasets for languages such as Spanish and Arabic [9][10]. A second contribution is the validation of location mention extraction using past disaster tweets posted in Japanese. In their earlier study, Suwaileh [6] et al. sought such validation for English language tweets; in our current study, we show that their approach can be adapted to Japanese heavy rainfall disasters described in tweets composed in Japanese.
<table><caption>**Table 1.** Disaster-related tweets that include location mentions</caption><thead><tr><th></th><th>Original Japanese text</th><th>Translated English text</th></tr></thead><tbody><tr><td>Tweet 1</td><td>大雨の影響で安芸矢口駅付近の道路が冠水・浸水 現地の様子&lt;URL&gt;</td><td>Roads near Aki Yaguchi Station flooded and submerged due to heavy rains. The situation there is the following. &lt;URL&gt;</td></tr><tr><td>Tweet 2</td><td>大雨で飯山線止まって北陸新幹線でしか長野に戻れなくなった&lt;URL&gt;</td><td>Heavy rain stopped the Iiyama line, so I could return to Nagano only by boarding the Hokuriku Shinkansen. &lt;URL&gt;.</td></tr></tbody></table>

<figure><img src="image_2.png" /><figcaption>**Fig. 1.** Adapting to ongoing disasters using inference models learned from past disasters</figcaption></figure>

# 2 Related Work

## 2.1 Analysis of Disaster-related Tweets

In recent years, there have been numerous attempts to facilitate the use of social media to disseminate information and assist early responders in times of disaster. Various researchers have proposed methods to solve the problem of selecting important information from the huge number of tweets posted during a disaster by treating it as a document classification problem. For instance, Kayi [11] et al. developed a method for estimating urgency in order to set disaster response priorities. A method that classifies disaster-related information into four genres ("damage situation," "warning," etc.) as a way to filter the information has also been proposed [12].

Chowdhury [13] et al. offered a method that takes into account the broken writing style of social media users, such as replacing "you" with "u", when extracting situationally important phrases from tweets. Studies are also underway for extracting location mentions from tweets. For example, Al-Olimat et al. proposed a method using open
data sources featuring the names of public facilities, tourist attractions, and other land-
marks [14]. Such studies are based on the assumption that it is possible to immediately
annotate tweets and build machine learning models to make inferences when a disaster
occurs. However, Medina [15] et al. argued that such an assumption is not practical in
a real catastrophe and discussed the possibility of using tweets posted during previous
disasters to help build an accurate model. As noted above, Suwaileh [6] et al. sought to
verify various aspects of location mention recognition. For example, they examined the
extent to which results differ when inferences are made using tweet data from the same
type of disaster and when inferences are made using tweet data from a different type of
disaster [16].

Importantly, disasters vary in frequency, scale, and type from country to country and region to region. For example, the study by Olteanu [7] et al. covers disasters in the U.S., Canada, and Australia, where the main disaster types are hurricanes and floods. On the other hand, the study by Hamoui [18] et al. targets sandstorms (as well as floods) due to the high frequency of dust storms in Saudi Arabia. In Japan, which is the target of this study, earthquakes are particularly large and frequent relative to other regions of the world, and floods and typhoons occur often.

There have been active studies in Japan focused on using Twitter during disasters, such as DISAANA [19] and D-SUMM [20]. Studies have also been conducted on the classification and analysis of disaster tweets. Yamada et al. [21] analyzed the number of tweets, trends in the use of hashtags and emojis, posts with a high number of re-tweets, and the number of tweets with URL links to news articles viewed over time for the torrential rains that occurred in 2018. Yamamoto [22] et al. analyzed and categorized tweets circulating during disasters, focusing on tweets that encouraged action. They point out that there are several types of sources of such information, including public outlets and hearsay. However, the main objective of our study is to extract information using past disaster data, which is different from the objectives of most of these other studies.

## 2.2 Location Mention from Twitter

Filtering information on Twitter according to the situation is especially important dur-
ing a disaster, where a vast amount of information is typically circulated in a short
period. The location information of tweets posted on Twitter is of particular importance.
There are three main methods for extracting such location information: The first method
is to use geotags, latitude and longitude information that can be added to tweets to iden-
tify the location of the poster; however, it has been reported that the number of ge-
otagged tweets tends to be extremely small [23]. The second method is to employ user
profiles [24]. This method uses the fact that user profiles (self-introductions) often in-
clude the area in which the user lives as well as their various affiliations. However, this
approach does not necessarily identify the location from which a particular tweet was
sent. The third method focuses on location mentions. This method extracts location
references such as landmark names that may be included in a tweet and uses them as
hints to infer an address [25][26][27]. However, identifying a specific location in this
way can be difficult since the same name may refer to multiple locations (for example,
"Central Park"). Given that each of these methods has its advantages and disadvantages,
using some combination of methods may be desirable. For example, if no geotag is
attached to a particular tweet, a location mention might be extracted; if there are multiple location candidates, the confusion might be resolved based on the user's profile. In this study, we focus on landmark names in tweets as the primary indicator of tweet location.

## 3 Problem Definition

Extracting location mentions refers to identifying all location mentions in a tweet. More specifically, it means to identify all words $w_i$ ($i = 1, \dots, L$) of a given tweet $t = \{w_1, w_2, \dots, w_L\}$ that refer to locations. Since location mentions can consist of multiple words, we consider a tweet as a series of words and treat the problem as a series labeling problem that predicts the class of each word. We define the five BILOU classes with reference to Yang [28] et al. Figure 2 gives an overview of the problem setup. Here, label "B" indicates the first word of a location mention; label "L" indicates the last word of the location mention. Label "I" indicates a word between the first and last words of the mention. Words that are not location mentions are assigned the label "O." A location mention consisting of only one word is labeled "U." In this study, we attempt to classify tweets into multiple classes based on this definition.

<table><caption>Fig. 2. Problem Definition Overview (*:particle)</caption><thead><tr><th>English<br>translation<br>sentence</th><th colspan="8">Power outage at a local community center in Mabi.</th></tr><tr><th>Token in<br>Japanese<br>(English)</th><th>真備<br>(Mabi)</th><th>の<br>(*)</th><th>地域<br>(local)</th><th>交流<br>(community)</th><th>センター<br>(center)</th><th>が<br>(*)</th><th>停電<br>(power outage)</th><th></th></tr></thead><tbody><tr><th>Label</th><td>U</td><td>O</td><td>B</td><td>I</td><td>L</td><td>O</td><td>O</td><td></td></tr></tbody></table>

## 4 Tweet Collection

The focus of this study is on three recent heavy rain events in Japan. We annotated tweets that we collected from around the time of these three events using the Twitter Search API. To exclude tweets unrelated to the three rain-related disasters, we conducted preliminary experiments based on the work of Paul et al. [29] and determined the queries to be used for the collection. The details are shown in Table 2.

### 4.1 Preliminary Experiment

Twitter can be classed as a microblog, characterized by short sentences (as compared to other social media platforms). Because of its length restrictions, subjects and parts of sentences are often omitted. Therefore, a single keyword such as "heavy rain" may not be sufficient to collect tweets referring to disaster-stricken situations such as flooding and landslides. In this study, we collected tweets with a large granularity and analyzed the results to determine keywords to gather more tweets. First, we collected
tweets using only the word "大雨" (which means "heavy rain") and selected 200 tweets that mentioned disaster. We used a morphological analyzer to divide the tweets into words and then aggregated their frequencies. The morphemes with exceptionally high frequency were often associated with lifelines and infrastructure damage and were used to help select tweets referring to the disaster.

## 4.2 Parameters for Tweet Collection

The information required immediately after a disaster strikes differs from that needed after some time has passed. In this study, which targets the immediate aftermath of a disaster, we collected tweets circulated within the 24-hour period beginning at the time the Japan Meteorological Agency issued a heavy rain emergency warning. As shown in Table 2, tweets were collected using queries that were manually selected and extended based on preliminary experiments, with start and end times as parameters.

<table><caption>**Table 2.** Conditions for tweet collection</caption><thead><tr><th>Disaster</th><th>Starting time</th><th>Query</th></tr></thead><tbody><tr><td>July 2018</td><td>July 6, 2018<br>17:10</td><td>大雨(heavy rain) OR 冠水(submerged) OR 泾濫(overflow) OR 水没(submergence) OR 增水(high flow) OR 浸水(inundation) OR 洪水(flood) OR 断水(water failure) OR 浩滞(congestion) OR 停電(outage) OR 避難所(shelter) OR 避難場所(salvage place) OR 救助(rescue)</td></tr><tr><td>July 2020</td><td>July 4, 2020<br>04:50</td><td>OR 通行止(road closed) OR 通行規制(traffic regulation) OR 通行不(no road) OR 土砂崩れ(landslip) OR 溢れ(inundate) OR 浸かつ(dip) OR 倒木(fallen tree) OR 孤立(isolation) OR 陥没(collapse) OR 落石(rock fall) OR 崖崩れ(landslide) OR がけ崩れ(landslide)</td></tr><tr><td>Aug. 2021</td><td>Aug. 13, 2021<br>08:45</td><td></td></tr></tbody></table>

## 5 Filtering Disaster-related Tweets

According to a study by Paul et al. [29], very few tweets actually mention disasters, even when they include various disaster-related keywords. In this study, we further narrowed the search to original tweets (tweets other than re-tweets) that satisfied the following conditions:

1. written in Japanese

2. retweeted at least once

3. posted using the official Twitter client

4. contain one or more location mentions

These conditions were set based on the hypothesis that tweets that were re-tweeted at least once contain useful information and that posts from the affected areas do not generally use non-official Twitter clients, unlike BOTs or specific web services.
**5.1 Written in Japanese**

Since the Japanese language shares characters with Chinese, we could expect that tweets written in Chinese but irrelevant to our study would be included in our search results. To address this problem, we used metadata included in the Twitter Search API responses to filter the tweets and restrict them to those written in Japanese.

**5.2 Retweeted at least once**

We assumed that the reason that a user re-tweets a tweet is that the tweet contains meaningful information that the user wishes to disseminate to a larger audience. (Re-tweeting duplicates an existing tweet and reposts it as the user's own tweet, making it available to the user's followers.) Based on this assumption, tweets that were not retweeted were excluded from the annotation since they appeared not to contain useful information. Preliminary experiments showed that tweets such as "*Heavy rain is falling in <LOCATION MENTION>*" or "*Heavy rain is falling, is <LOCATION MENTION> safe?*" are of low importance in disaster response.

**5.3 Posted using the official Twitter client**

We sought to identify highly immediate information from users who were directly affected the disaster. However, since Twitter includes BOTs that periodically post canned messages and posts from Web services such as news sites that organize information after the fact, we needed a way to exclude such posts as irrelevant to our purpose. Based on the hypothesis that, unlike BOTs and various Web services, users posting from disaster-affected areas do not use unofficial Twitter clients when posting, we selected only tweets posted using official Twitter clients. (Preliminary experiments confirmed that tweets whose client was twittbot.net, a BOT hosting service, and tweets posted by NHK, a Japanese public broadcasting organization, were excluded.) The accepted Twitter clients were:

* Twitter for iPhone

* Twitter for iPad

* Twitter for Android

* Twitter Web Client

* Twitter Web App

* TweetDeck

**5.4 Contain one or more location mentions**

As noted, in order for disaster responders to make important decisions based on tweets, it is necessary to identify the location of the tweets. However, location mentions can range from widely known proper nouns, such as prefecture names, to abbreviations used only in certain regions. In this study, we built a dictionary of location mentions by combining different types of open data, following the work of Al-Olimat [14] et al. The dictionary was used to determine if there is a location mention in a tweet. If there are
multiple candidate entries in the dictionary for a mentioned location, the longest candidate is selected as the location. For example, if a tweet contains the sentence "Yokohama station is flooded" and the dictionary has as location candidates Yokohama and Yokohama station, Yokohama station would be selected.

To collect a wide range of location mentions, we used three types of open data. First, we used geonames.jp (https://geonames.jp/), an open data source that contains the names of Japanese prefectures and allows us to obtain place name reference expressions with relatively large granularity. Next, we used OpenStreetMapJapan (https://open-streetmap.jp/) to collect specific landmarks, such as hospitals and commercial facilities. Finally, we extended the map data from OpenStreetMapJapan with DBpedia Japanese (https://ja.dbpedia.org/), an open data source that extracts information from Wikipedia and supports not only the official names of various landmarks but also their abbreviations and spelling variants. After integrating these three types of open data, we applied post-processing to format the data and used the formatted data as our place name reference dictionary. In addition, two post-processing steps were applied to the dictionary to avoid matching to unintended substrings and to accommodate more expressions in Twitter shards.

**Filtering location mention.**

Due to the nature of editing by an unspecified number of people on the web, open data often contains typographical errors. Therefore, some entries were removed manually. There were also cases where entries in the dictionary matched non-location mentions, e.g., the names of people. These were also removed manually, as necessary. Some entries were removed mechanically using criteria such as character type and number of characters.

**Data Augmentation.**

As noted, we used open data (DBpedia) covering abbreviations and contractions in our study. However, we sought to extend the entries in the location mention dictionary to include an even wider range of expressions. For example, in Japanese, “high school” is formally written as "高等学校," but the abbreviated forms "高校" and "高" are commonly used. Therefore, if there was a dictionary entry for "A 高等学校," "A 高校," and "A 高" were added to the dictionary.

The dictionary created by the above steps was used to exclude tweets that did not contain a location mention.

# 6 Annotation

The tweets remaining after applying the filtering methods described above were annotated with location mentions. For the annotation work, we referred to the scheme proposed in the study by Matsuda et al. [25]. Table 3 shows examples of the location mentions annotated in this study. Several types of location mentions were targeted, including "Location," which has a large granularity (e.g., prefecture and municipality),
and "Facility," which has a smaller granularity (e.g., station and university). We annotated these place name reference expressions with BILOU tags.

Tweets that did not refer to the disaster-stricken area during the annotation process were excluded. For example, even if a tweet was matched to a dictionary entry for a location mention during the tweet filtering process, the tweet was excluded if the reference was to a specific person rather than, for example, a river. (The name of a river (e.g., 荒川 (Arakawa river)) is also a family name in Japan.) We also removed tweets that did not report damage but only expressed concern over the situation in the affected areas. These processes were repeated until 1,000 tweets were identified for each disaster. As a rule, annotating the results of random sampling without refinement is preferred since it is conceivable that specifying conditions may cause bias. However, as shown in Table 4, a massive number of tweets circulate after a disaster. Thus, we used the conditions mentioned above to filter the tweets.

<table><caption>**Table 3.** Annotated location types</caption><thead><tr><th>Type</th><th>Original Japanese words</th><th>English translation words</th></tr></thead><tbody><tr><td>Location</td><td>岩手県 倉敷市</td><td>Iwate prefecture, Kurashiki city</td></tr><tr><td>Facility</td><td>博多駅 東海大学</td><td>Hakata station, Tokai university</td></tr><tr><td>Railway</td><td>京浜東北線 山手線</td><td>Keihinohoku line, Yamanote line</td></tr><tr><td>River</td><td>球磨川 阿武隈川</td><td>Kuma river, Abukuma river</td></tr><tr><td>Road</td><td>国道4号線 東北道</td><td>National Route 4, Tohoku expressway</td></tr><tr><td>General</td><td>病院 コンビニ</td><td>Hospital, Convenience store</td></tr></tbody></table>

<table><caption>**Table 4.** Aggregate results for each process</caption><thead><tr><th>Disaster</th><th>Collected tweets</th><th>Filtered tweets</th><th>Annotated tweets</th></tr></thead><tbody><tr><td>July 2018</td><td>1,107,378</td><td>26,780</td><td>1,000</td></tr><tr><td>July 2020</td><td>561,311</td><td>12,890</td><td>1,000</td></tr><tr><td>August 2021</td><td>441,930</td><td>16,186</td><td>1,000</td></tr></tbody></table>

# 7 Experiment

## 7.1 Experimental setup

We used tweets from past disasters in the learning and inference phases to test the feasibility of using such tweets. For the learning phase, we used tweet data from either the July 2018 torrential rainstorm, the July 2020 torrential rainstorm, or an equal mix of both. In the inference phase, we used tweets from the August 2021 torrential rainstorm. We did this in order to assess the impact of using either a single disaster or a combination of disasters.

BERT [31] was used to extract location mentions. BERT is widely used as a baseline for the series labeling problem and is known to give good results even with relatively small amounts of data. There are two ways to adapt BERT to a particular language: monolingual models trained on a single language and multilingual models
(mBERT) trained on multiple languages. Although mBERT adapts to multiple lan-
guages with a single model, models that focus on languages with the same features can
produce better results. For example, Xu et al. [32] proposed a model trained only on
Japanese and Chinese, using the fact that Japanese and Chinese share certain features,
such as identical characters and non-trivial boundaries between words. As a result of
validation, Xu et al. [32] reported improved performance in tasks such as translation in
Japanese and Chinese. It has also been shown for multiple languages that models
trained on only a single language are effective when there is no need for a single model
to support multiple languages, as in translation [33][34]. However, since learning
BERT from scratch is very computationally intensive, we decided to fine-tune our ap-
proach based on BERT-japanese (https://github.com/cl-tohoku/bert-japanese), a ver-
sion of BERT specialized for Japanese. BERT-japanese is trained using Japanese Wik-
ipedia and is highly versatile, as it covers a wide variety of fields. In addition, BERT-
japanese has been employed in numerous studies targeting the Japanese language
[35][36][37]. Finally, although there are improved models of BERT, such as RoBERTa
[38] and ALBERT [39], with enhanced performance and speed, we chose BERT to be
consistent with previous studies.

The data were prepared using the following the steps: First, URLs and user names in
tweets were replaced with <URL> and <USERNAME>, respectively, as a pre-pro-
cessing step. The morphological analyzer MeCab (https://taku910.github.io/mecab/) was used for word segmentation. The dictionary used in this study was unidic-lite (https://github.com/polm/unidic-lite). Experiments were conducted with a 0.8 / 0.1 / 0.1 ratio for training, development, and testing, respectively. The hyperparameters for training were set based on the original paper [31], and AdamW was selected as the optimizer, with a learning rate of 2e-5 and a batch size of 8, trained in 3 epochs.

7.2 Experimental results

Table 5 shows the experimental results. Case (1) can be considered the ideal case, where
data are available immediately after the occurrence of a disaster. Cases (2) through (4)
are cases where data from past disasters are used.

Comparing Comparing the results of cases (2) and (3) shows the relative perfor-
mance when data from a single disaster were used. As can be seen in the table, case (3)
shows better results. In case (3), the heavy rain emergency warning was issued at 5
p.m.—a time when many Twitter users would be traveling home or commuting to work.
Thus, the available tweets could be expected to include real-time information from the
disaster-stricken areas regarding the damage being observed, such as flooding caused
be overflowing rivers or road closures due to landslides. In contrast, case (2)'s weather
event occurred in the early morning, before 5:00 a.m., which meant that the number of
tweets from the affected areas was limited and that there was little variation in content.
As a consequence, the model did not produce good generalization results.

Case (4) uses a combination of the data from the two disasters featured in cases (2) and (3). Random sampling was used to produce the combined sample, with half the tweets selected from case (2) and half from case (3). As shown in the table, performance in case (4) is better than in either case (2) or case (3), suggesting that the two cases are complementary. In other words, generalization performance can be expected to improve by combining data from multiple disasters rather than learning from only one.
Finally, by comparing case (1) and case (4), we can assess the extent to which in-
ference results differ between the ideal case, where current data are available immedi-
ately, and the case where only past disaster data are available. As shown, there is a gap
of nearly 10 points in each of the indices, strongly suggesting that there is still work to
be done to achieve performance close to that in the ideal case.

<table><caption>**Table 5.** Experimental results</caption>
  <thead>
    <tr>
      <th></th>
      <th>Training Data</th>
      <th>Test Data</th>
      <th>Precision</th>
      <th>Recall</th>
      <th>F-Score</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>(1)</td>
      <td>August 2021</td>
      <td></td>
      <td>0.789</td>
      <td>0.827</td>
      <td>0.808</td>
    </tr>
    <tr>
      <td>(2)</td>
      <td>July 2021</td>
      <td rowspan="3">August 2021</td>
      <td>0.577</td>
      <td>0.625</td>
      <td>0.600</td>
    </tr>
    <tr>
      <td>(3)</td>
      <td>July 2018</td>
      <td>0.626</td>
      <td>0.692</td>
      <td>0.657</td>
    </tr>
    <tr>
      <td>(4)</td>
      <td>(2) + (3)</td>
      <td>0.666</td>
      <td>0.731</td>
      <td>0.697</td>
    </tr>
  </tbody>
</table>

8 Discussion

Given It seems self-evident that it would be better to use tweets posted during a current disaster for model learning versus tweets from past disasters. However, rapid decision-making and immediate action are critical to ensuring an effective disaster response. On the other hand, there is a need to allow sufficient time to properly prepare potentially crucial data in order to produce accurate inferential results. The conflicting nature of these two considerations means that a trade-off is required. One approach is to use a model learned from past data immediately after the event and to replace it once the current data have been properly prepared. Alternatively, the two models (past and present) can be combined. The point is that conducting research in parallel in order to take advantage of the merits of both is highly desirable.

When using past tweets as the source of data, improving generalization performance is key. In addition to combining data, a number of possible ways to achieve better performance have been suggested. One is to focus on tweets with images and to combine text with image information [40]. Research is also underway to adapt multi-task learning, which attempts to improve generalization performance by allowing a single model to learn to solve multiple tasks, to disaster tweets [41]. Other proposals include improving the BERT models. Wang et al. [42] reported that even naive models such as word2vec [43] are better at representing tweet features when trained on tweets, compared to BERT models trained on Wikipedia. In support of this result, Nguyen et al. [44] proposed BERTweet, which is trained on tweets, and reported improved performance when targeting tweets. Kawintiranon et al. [45] developed PoliBERTweet, which is trained on tweets about politics, and suggested that further performance improvement can be expected by specializing in a particular field. We believe that these variations can be similarly adapted for disaster tweets.

A comparison of the classification performance obtained by Suwaileh et al. [6] with that obtained in this study showed similar results. It is interesting to note that these similar results were produced for Japanese tweets about disasters that occurred in Japan and for English tweets about disasters that occurred overseas. Further study to determine whether the error tendency is the same for Japanese and English tweets due to
differences in characters, grammar, etc., appears warranted but is left for future inves-
tigation.

9 Conclusion

Identifying the location of relevant disaster-related messages posted on social media is
necessary in order to utilize the information contained in those messages to help for-
mulate and implement a proper disaster response. This study examined the recognition
of location mentions in tweets posted on Twitter using a model learned from tweets
posted during past disasters and verified it using tweet data from three previous heavy
rain disasters in Japan. When using past disaster tweets for training, we found that the
best performance is obtained when data from multiple cases are combined. As part of
our future work, we plan to test whether the same results can be obtained for other
heavy rainfall disasters and to examine how performance changes when data from dif-
ferent kinds of disasters, such as earthquakes and typhoons, are combined.

**Acknowledgments.** This research was supported by JSPS KAKENHI Grant Number 18K11553.

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