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Automated Processing for Social Media Data in a Mass Emergency</h2>
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<p> The world is full of emergencies caused by natural disasters. In such situations, vast
amount of
information
will be exchanged via social media networks (Facebook, Twitter, etc), official websites and
public forums
which are
dedicated for management of natural disasters. In countries where natural disasters are
frequent,
disaster management
centers and disaster management coordinating units have employed teams to monitor and
analyze
information
to obtain a
closer insight into a particular situation. It helps to identify areas that have suffered
the
most in an
emergency,
the type of emergency, immediate needs of victims, casualties and infrastructure damages.
Manually
analysis of
overwhelming amount of information is difficult and time consuming. Real-time disaster
information is
critical for rapid
decision-making in response to emergencies.</p>
<p>The term “social media” refers to Internet-based applications that enable people to
communicate and
share resources and information[10]. Social media plays a major role in current
society.
As of 2018
it is the streamline communication medium to share not only text but also images,
voice
and video
information. Millions of users have made it a daily practice to surf through social
media to find and
share information [5]. Researchers have found a burst of information generated
during
and aftermath
of a mass emergency through social media platforms as a useful resource to get an
insight into the
situation which is known as situational awareness.
On August 24th 2014, 3:20 am Pacific time, an earthquake of magnitude 6.0 occurred
in
the Bay Area,
3.7 miles (6.0 km) northwest of American Canyon near the West Napa Fault. Figure 1
shows
the Facebook
activity on August 17th 2014 (Green) and August 24th (Purple) the day earthquake
occurred. Spike of
activity in between 3am and 6am. [4] This shows the notable difference of social
media
activity
between a regular day and otherwise.
The use of social media for emergencies divides into two categories. Firstly,
social
media is used to
passively disseminate information and receive feedback through messages, wall
posts, and
comments.
Secondly an approach involves the systematic use of social media as an emergency
management tool.
Systematic usage might include using the medium to conduct emergency communications
and
issue
warnings; using social media to receive victim requests for assistance; monitoring
user
activities to
establish situational awareness; and using uploaded images to create damage
estimates,
among
others.[10] Most Disaster Management organization’s only use of social media is to
share
information
among others.
In an emergency such as earthquake there can be posts which contain facts related
to the
extent of
the affect, infrastructure damages, casualties, donations available or requested,
the
kinds of
necessities required, for instance food or water [1]. Trustworthiness and
reliability of
incoming or
extracted information is a matter that is yet to be solved. Formal first
responders,
disaster
managers humanitarian organizations, NGOs, general public, local police, area
firefighters are few of
the stakeholders who benefit from such information. Moreover, different types of
information sought
by different stakeholders. For example, humanitarian organizations might be
interested
in potential
donors and the requirements of the victims while first responders and disaster
managers
are
interested in the infrastructure damages and casualties. To make use of this
information
generated
some organizations has employed teams to analyses and generate reports which are
useful
for first
responders to make time critical life and death decisions [1]. For example,
American Red
Cross, they
have a unit called Digital Operations Center specifically for this purpose. Reading
and
manually
analyzing continuous streams of unorganized textual information generated at an
increased rate is a
hard and time-consuming task. Responding to each incoming message timely might
require
increase of
employees. Therefore, processing social media data during a mass emergency is a
matter
that requires
new ways of data processing reducing the amount of information examined by the
humans.
Although there exists a potential need to use social media platforms to serve this
kind
of specific
purposes as mentioned above, some countries, organizations have failed to or
hesitant to
appreciate
the benefits and the widespread usage of social media to improve situational
awareness.
The rest of
the paper discuss about a new flow of process for processing social media posts in
real
time as well
as aftermath of an incident. Following content is based on work in progress
research.
Purpose of the
document is to present the approach, current status and future works of the
proposed
solution. Other
than the academic research value there are much more value addition for real world
scenarios.
Convincing and persuading the stakeholders to use this type of system by producing
a
viable tool for
them to use efficiently and effectively contrasts among the other main objectives.
</p>
<img src="static/img/table1.PNG"/>
<img src="static/img/table2.PNG"/>
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Research gap
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<p>Even though there are existing products in the market with similar objective, they
don’t
address
several
problems that needs to be taken into consideration. From the literature survey and
research
activities
carried out, we discovered that there is quite a research gap that needs to be
addressed. The
following table shows a comparison of features between existing products and the
proposed solution.</p>
<img src="static/img/differnce.PNG">
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Research
Problem
</div>
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<p>
Researches has found a serge in social media activity during emergency
Processing social media posts manually is hard, during disaster time it’s critical
Accuracy and the dependability of a given entry is the lacking feature of current
systems available
Finding actionable, tactical and informative information from social media has been
a
serious challenge
</p>
</div>
<!-- feature-content -->
</div>
</div>
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<div class="font-alt font-25 font-w-600 letter-spacing-1 m-15px-b color-extra-dark-gray">
Objectives
</div>
<div class="font-15 font-w-600 font-alt m-15px-b">Main Objectives</div>
<p>The proposed system is a research project focused on several objectives. Upon the
completion of the
project,
we are supposed to fulfil these research objectives. The general objectives of the
research project
are
mentioned as follows: </p>
<div class="font-15 font-w-600 font-alt m-15px-b">Specific Objectives</div>
<p>1. Developing an automatic text summarization component for processing social media
posts
in an emergency and generate related summaries.</p><br>
<p>2. Categorizing the information identified and prioritizing the information of
social
media posts in order to obtain filtered information.</p><br>
<p>3. Sentiment analysis of information to measure how critical, the corresponding
situation
could be.</p><br>
<p>4. Validating the accuracy of each social media post by analyzing the follow up
comments.</p>
</div>
</div>
</div>
</div>
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<div class="font-alt font-25 font-w-600 letter-spacing-1 m-15px-b color-extra-dark-gray">
Methodology
</div>
<p>
Trend identification heleps to detect major events happening around the wolrd. To
archieve this we propose a threshold based algorithm which will give its output as
word clouds. The generic way is to count the most frequent words from text. When
applied Bag of words algorithm for text we get the words and their frequencies. A
word cloud ( example Fig 3) can be generated hourly or minute by minute for better
clarity . But to do this it costs more computational power.
</p>
<p>
Entry Filter is responsible for identifying (Filtering) relevant posts. During a
natural disaster relevant information are generated by eyewitnesses, people who are
affected (victims), government and humanitarian groups/ agencies, people who wants
to donate and people who are willing to take part in voluntary services. Processing
these entries in a timely manner is a reoccurring matter in the disaster management
aspect as this information get obsolete very quickly. To make use of the valuable
information included in the entries (Posts) and to realize or visualize
information, this component is introduced to the system. Filtering is a critical
sub component since it is the starting point of the flow of automated processing
for social media entries.
</p>
<p>
Social media is one of the major ways of dispersing information during a disaster
situation. Sharing false information would lead to bad consequences, so that the
validation of dependability of social media posts is crucial in a natural disaster.
Studies have found that outdated, inaccurate, or false information has been
disseminated via social media forums during disasters [8]. In some cases, the
location of the hazard or threat was inaccurately reported. In the case of the
March 2011 Japanese earthquake and tsunami, tweets for assistance were retweeted
after the victims had been rescued [9]. The level of accuracy determines the level
of dependability and trustworthiness of a piece of information extracted using the
data generated during an emergency. This also involves analyzing metadata of
entries such as number of comments shares and likes. If follow up comments are
supporting the main entry them it can be considered as an entry with higher
dependability. Validation is a huge concern due to the misuse of social media to
disperse of false information Posts that fails from this stage will not move to the
next stage.
</p>
<p>
The evaluation of sentiment and the extent of effect of a post or the level of
criticality will be important when referring to this kind of scenario. For example,
if a post contains a request for immediate help that can be considered as a high
priority. Intention of this component is to help the first responders with their
critical decision making. Most of the time they must make important decisions
rapidly during an emergency. They must decide what needs to be done, which issue
needs to be attended first. So, by giving a level of importance high, medium or
low, they will be able to get a chance to attend high priorities by spending less
time.
This is done using three separate datasets with labels “Low”, “Medium” and “High”
and training a model using supervised machine learning.
</p>
<p>
Summarization is focused on identifying the core meaning of a post and extracting
the summarized content over bulk of social media posts. It is important to
maintaining the core meaning of a post without damaging the actual meaning of it.
This volume of text is an invaluable source of information and knowledge which
needs to be effectively summarized to be useful [7] for the natural disaster
supporting teams to take actions timely, effectively and efficiently.
Automatic text summarization is challenging, since as humans we summarize a piece
of text by reading it entirely to develop our understanding, and then write a
summary highlighting its main points. Since computers lack human knowledge and
language capability, it makes automatic text summarization a very difficult and
non-trivial task[7].According to Radef et al. [6] a summary is defined as “a text
that is produced from one or more texts, that conveys important information in the
original text(s), and that is no longer than half of the original text(s) and
usually, significantly less than that”. Automatic text summarization is the task of
producing a concise and fluent summary while preserving key information content and
overall meaning. [7]
Text summarization approaches is divided into two groups: extractive and
abstractive summarization. Extractive summarizations extract important sentences or
phrases from the original documents and group them to produce a summary without
changing the original text. Abstractive summarization consists of understanding the
source text by using linguistic methods to interpret and examine the text.
Abstractive methods need a deeper analysis of the text. These methods can generate
new sentences, which improves the focus of a summary, reduce its redundancy and
keeps a good compression rate.
Summaries produced by extractive summarization techniques are constructed by
choosing a subset of sentences in the original text which is being the input for
the text summarizer. The chosen sentences are supposed to be most important
sentences of the input text corpus. According to the context of the research, the
input text could be a social medial post with follow up comments. Extractive
methods tend to be verbose and this is especially problematic as produced
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<p>The project proposal is the main interaction between the client and the product maker.
It
provides
what the
system provides, cost and the intial steps to follow when it is made. In the interview
panel, it
consists
of 3 intellectuals who are experted in the fields that are relevant to the
functionalities
of the
system
that is proposed.</p>
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<p>The SRS presentation is the opportunity that is to interact with the Research Supervisor
and
show the
separate
functionalities of the system of the individuals.</p>
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<p>The progress presentation 01 or Prototype presentation is the situation where the system
is
at 50%
completion.
The basic functionalities of the individuals should be working and the whole system is
not
needed.</p>
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Progress
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</div>
<p>progress presentation 02 refers as the situation where the whole system should be
working at
the level
90%
overall. The functionalities should be working together so that the decisions on the
final
product can
be identified by executing the system from start to end.</p>
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<p>Final presentation is the point where the system is fully functional at 100%. Which
means
that the
system
must be a full product that is able to commercialize.</p>
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<br> (12th
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<p>Individual assessment of each member of the group to identify the level of understanding
of
the system
and
the functions of the system.</p>
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