=Paper=
{{Paper
|id=Vol-1737/T2-10
|storemode=property
|title=Information Retrieval from Microblogs during Natural Disasters
|pdfUrl=https://ceur-ws.org/Vol-1737/T2-10.pdf
|volume=Vol-1737
|authors=Roshni Chakraborty,Maitry Bhavsar
|dblpUrl=https://dblp.org/rec/conf/fire/ChakrabortyB16
}}
==Information Retrieval from Microblogs during Natural Disasters==
https://ceur-ws.org/Vol-1737/T2-10.pdf
Information Retrieval from Microblogs during Natural
Disasters
Roshni Chakraborty Maitry Bhavsar
Indian Institute of Technology Indian Institute of Technology
Patna, Bihar Patna, Bihar
India India
roshni.pcs15@iitp.ac.in bhavsar.mtcs15@iitp.ac.in
ABSTRACT and conclusion in section 5.
In this paper, we devise an information retrieval system
which can filter and rank tweets according to relevance to 2. DATA COLLECTION AND
the query. We devise methods to understand relationships PRE-PROCESSING
among entities and action verbs from a small set of manually
FIRE Microblog Track provided a set of about 50,000
annotated tweets. We further use these relationships to filter
tweet-ids which we used to the access the tweets. We fil-
tweets and rank them accordingly. Our results (as published
tered only the relevant tweet information from these tweets,
by FIRE Microblog Track) show that we have high precision
that consist of tweet text, tweet id, etc. We further filtered
score in detection of topmost 20 tweets.
some tweets from the whole set of tweets. For example,
during disasters, there are a number of tweets that express
1. INTRODUCTION grief, urge people to pray or help. These messages are gen-
FIRE 2016 Microblog track [1] provided us with about eral messages, hence we made a bag of words that express
50,000 tweets related to Nepal Earthquake of April 2015. only urge, request, pray etc. and removed those tweets that
In this paper, we segregate tweets into different categories, contain words from this bag.
namely, availability of resources, requirement of resources,
availability of medical resources and facilities, requirement 3. METHODOLOGY
of medical resources and facilities and information related
In this section, we discuss our procedure. We do not use
to infrastructure destruction or restoration. We devised a
any external source of information. We use NLTK toolkit1
mechanism to learn text attributes of tweets to segregate
to perform text based analysis on tweets. We rely on tweet
them into specific categories.
text attributes to filter tweets of relevance. In order to
We manually annotate a random sample of 1000 tweets
understand the text attributes of tweets, we select a ran-
into specified categories of information, a tweet can also be-
dom sample of 1000 tweets from the whole set of 50,000
long to multiple groups. For example, a tweet of destruc-
tweets. We manually group tweets according to different
tion of a bridge also might convey information about the re-
queries by FIRE, a tweet can nevertheless belong to differ-
quirement of basic amenities. Hence, different categories of
ent categories.
tweets had different text attributes that pertain to a specific
We perform a set of operations on tweet text for every
information related to that query. We aimed at identifying
group (as specified before). Firstly, we remove the stopwords
those text attributes, i.e, combination of different words for
from these tweets. Stopwords hardly represent any special
any particular query. We further created networks of each
characteristic of an entity. After removal of the stop words,
query’s text attributes’ combinations. The edges represent
we use POS Tagger to select only the nouns and verbs from
the interrelationships among these text attributes which aid
the tweets. We then rank the entities of all tweets according
in segregation of tweets according to different queries. We
to frequency. We select a subset of these entities according to
will describe the methodology in details in later sections.
the ranks, we also include the entities specified in the query
Tweets are informal, so a vocabulary gap exists even among
itself by FIRE. This step gives us a list of the important
tweets of same strata. So, we did not depend only on text
entities for a specific query.
analysis of named entities, like food packets but rather com-
Often, an entity to entity matching fails to resolve tweets
bined them with the set of important verbs that identifies
of different genre, i.e., a tweet containing information of
a correct relationship among those. We weighed the differ-
medical aids can either highlight availability or requirement
ent identified keywords of each category according to their
of the same. So, we identify the different set of possible
relevance to the query. We, thereby, could identify tweets
actions of any entities, to understand the underlying rela-
due to their presence of relevant keywords for a query. The
tionships. We further rank the bigrams to identify the set
published results from FIRE suggest we could accurately
of working verbs to highlight specific actions. Thus, this
identify tweets of high relevance of different categories with
set of related working verbs and entities signify tweets of a
good precision and recall.
particular category.
We have divided the paper into following sections. We
However, there remains a vocabulary gap among different
discuss about data collection and pre-processing in the next
tweets of even same category due to their informal structure.
section, followed by our procedure of identification of tweets
1
in section 3 and finally results and discussion in section 4 www.nltk.org
ually annotated tweets into similar groups. The main action
verbs represent donations, transport, relief inf ormation,
build. We represent the relationships between these different
set of action verbs with different set of entities in the graphs
1 and 2 and the set of keywords of each group in table 1.
Thus a new tweet is selected if it contains the existing rela-
tionship, as mentioned by the arrow, i.e., it must contain at
least an entity and verb from the nodes the arrow connects.
Words Representing
Node Name
the Nodes
Green4 off to nepal
survivor, victim,
Green5
affect
food, water, cloth,
blanket, biscuit,
Green6 power, plane, bus,
material, beef,
equipment
volunteer, helicopter,
Green7
Figure 1: Graph Relationships of Resource Avail- item, tool, app
ability Information Green8 team
shelter, tent, house,
Green9
home
Green1 0 relief, rescue
Blue1 donate
transfer, sell,
distribut, suppl,
send, sent, deliver,
Blue2
dispatch, offer, land,
deploy, transport,
prepar
relief, rescue,
working, aid,
Blue3
support, engage,
rush
Blue4 build
Figure 2: Graph Relationships of Resource Require- Blue5 need, want, require
ment Information
Table 1: Word Dictionary of Resource Availability
and Requirement Related Information
Tweets of both requirement and availability of medical re-
sources may contain entities, like blood and working verbs
like donate but are completely different in meaning. Hence, 3.1 Requirement of Resources
segregation only on the basis of keywords fails to differen- In this section, we intend to filter all tweets that mention
tiate these relationships. We analyze the context of those the requirement or need of some resource, like human re-
keywords relationships, which reflects the actual meaning, sources or infrastructure like tents, water filter, power sup-
as in the absence of question tags (like, where, how, what, ply, etc. We studied our manually annotated tweets, and
etc), or request tags (please, etc) in availability based tweets. found the main action verbs that denote requirement of re-
The segregation of tweets into different categories thus re- sources are, need related or relief related. We highlight the
quires identification of proper entities, actions, and context different relationships among these various entities in figure
to understand it’s relevance. 3 and include details of the different terms in table 1. Thus,
We further have ranked an entity and the action verbs we later select those tweets from the total list of tweets if
according to their importance, which we will explain later. it contains the relationship represented by the arrow, i.e., it
We formulate separate bipartite graphs for each query, that contains at least an entity and action verb from the list of
represents the relationships among the entities, actions and keywords that the arrow connects.
context. While a set of nodes represent entities’ names, an-
other set of nodes represent the names of verbs (i.e., actions). 3.2 Availability of Medical Resources
These relationships were formulated from the manually an- In this section, we identify messages that mention the
notated tweets. We give a brief overview of the specific availability of some medical resources like blood, blood bank,
words and their relationships for each query in the next sec- medicine, etc. Firstly, we distinguish different action verbs
tion. from the manually annotated tweets that contain informa-
We select the different types of action verbs from our man- tion related to this query, the verbs are namely donation,
Figure 3: Graph II Relationships of Medical Re-
source Availability Information
Figure 5: Graph Relationships for Devastation Re-
lated Information
3.3 Requirement of Medical Resources
In this section, we identify messages that mention the re-
quirement of some medical resources like blood, blood bank,
Figure 4: Graph Relationships of Medical Resource medicine, etc. We represent the actions and their corre-
Requirement Information sponding entities in graph 5, the arrows represent the rela-
tionships among the both. The table 2 represents the set of
keywords for each entity or action. Thus, we filter all those
transport, rescue etc. There are some action verbs that are tweets from the whole set of fifty thousand tweets which
ambiguous in meaning, example need reflects both the need contain the relationships, i.e., at least a keyword from both
and the availability of resources. On further analysis of need the nodes of an arrow.
mentioned tweets, we found need is used in availability of
resources tweets only in conditional statements (example, if
3.4 Infrastructure Damage And Report
is a conditional clause). of Restoration
We represent the actions and their corresponding entities In this section, we identify messages that mention the
in the next two graphs, namely graph 4 and graph 2, the damage or restoration of any communication or structural
arrows represent the relationships among the both. The infrastructures. However, the general statements about any
table 2 represents the set of keywords for each entity or structure is not relevant. We filter the possible set of in-
action. Thus, we filter all those tweets from the whole set frastructure names from our manual annotated tweets and
of fifty thousand tweets which contain the relationships, i.e., the different set of actions related to them. After detection
at least a keyword from both the nodes of an arrow. There of the relationships among the action verb and entity name
are also some stringent relationships, that comprise of more from the manually annotated tweets, we select only those
than just an entity and action name, as illustrated in graph tweets that contain We visualize the different relationships
4. among different set of entities in Figure 6, and highlight the
set of keywords in table 3.
Words Representing
Node Name Words Representing
the Nodes Node Name
blood, bloodbank, the Nodes
Green5 medicine, medical, hotel, debris,
doctor building, temple,
healthcare, hospital, rubble, tower, road,
Green6 bridge, house,
patient, diabities
provide, survivor, Green1 railway, dam, tent,
Green7 heritage, monument,
victim,affect
Blue1 donate, donated power grid, engineer,
equipment,
reach, transfer, sell,
electricity
distribut, suppl,
send, sent, deliver, reduce, flatten,
Blue2 destroy, devastat,
dispatch, offer, land,
deploy, transport, avalanche,
Blue1
prepar, continu damage, restore,
capture, collapse,
rescue, relief,
Blue3 build, builds
support, engag, rush
devastat, terrif,
Blue4 need, want require Blue2
heartbreak
call, contact,
Blue5 footage, image,
helpline Blue3
picture
Table 2: Word Dictionary of Medical Resource
Table 3: Word Dictionary for Devastation Related
Availability and Requirement Information
Information
Node Name Words Representing the Nodes Score
Action1 relief, rescue, aid 0.10
Action2 build, transfer, sell, distribut, send, sent, deliver, supply, donat, need 0.4
Action3 deploy, dispatch, lad, transport, fly 0.3
Action4 prepare, offer, launch, allow, provide, make, support, engag, rush, help, working, in action 0.2
Entity1 volunteer, food, biscuit, shelter, tent, house, home, cloth, blanket 0.7
Entity2 power, equipment, material, item, team, helicopter, bus, plane, call, helpline,contact 0.5
Table 4: KeyWord Relevance Score
4. SELECTION OF TWEETS 1. Precision at rank 20, i.e., considering up to the top 20
The above graphs represent different entities, and their set tweets for each topic.
of actions for a particular query. For a given query, we match
2. Recall at rank 1000.
the relationships among the new tweet with the prescribed
relationships. Thus, a tweet is selected if it contains the 3. Mean Average Precision at rank 1000.
specified relationships of entities of that query. We further
rank those tweets according to it’s relevance to the query in 4. MAP overall, i.e., considering all tweets retrieved in
the next section. the run.
4.1 Score of Tweets 6. CONCLUSION
In this section, we rank the selected tweets by their rel- In this paper, we devise a mechanism to extract the con-
evance to query. In order to rank the tweets, we score the textual, content relationships of entities. We are able to fil-
different keyword relationships of a query. The keywords ter tweets of high relevance for different queries by matching
are segregated into two different sections, entities and ac- these relationships. We require a small number of manual
tion verbs. We give importance to words that signify better annotated tweets to attain our results.
temporal relevance than others, i.e., there is a major differ-
ence between tweets like food items sent to affected areas by
Indian government, India dispatched 500 packets of rice to
7. REFERENCES
Nepal and India will dispatch food packets by saturday. We [1] S. Ghosh and K. Ghosh. Overview of the FIRE 2016
give a brief description of our scoring mechanism. Microblog track: Information Extraction from
Microblogs Posted during Disasters. In Working notes
1. T emporal Importance : An action verb is given more of FIRE 2016 - Forum for Information Retrieval
importance if it highlights immediate action rather Evaluation, Kolkata, India, December 7-10, 2016,
than future. This is illustrated by Action2 and Action3 , CEUR Workshop Proceedings. CEUR-WS.org,
Action4 . December 2016.
2. Relevance : Some action verbs, represent greater rel-
evance in times of calamity, as expressed in Action1 .
Similarly, there are some entities (as in Entity1 ), which
are the basis needs of human livelihood, like food and
shelter which are more important than information re-
lated to other entity (as in Entity2 ).
The different scores of the keywords are given in table 4.
Thus, a tweet’s score is the summation of it’s keywords’
scores. We hereby, could rank the tweets by their relevance
score accordingly.
Metric Name Result
P recision@20 0.770
Recall@1000 0.4344
M AP @1000 0.2186
OverallM AP 0.2208
Table 5: Result
5. RESULTS
In this section, we highlight our results, FIRE Microblog
Track matched our selected tweets with a manual annota-
tor’s results. We briefly give an explanation of the metrics
and our results are depicted in Table 5. The metrics are.