scholarly journals CIME: Context-aware geolocation of emergency-related posts

2021 ◽  
Author(s):  
Gabriele Scalia ◽  
Chiara Francalanci ◽  
Barbara Pernici
Keyword(s):  
Social Media ◽  
Social Network ◽  
Emergency Management ◽  
State Of The Art ◽  
Context Aware ◽  
Rapid Mapping ◽  
Emergency Event ◽  
Crisis Mapping ◽  
Indirect Information ◽  
Geolocation Accuracy

AbstractInformation extracted from social media has proven to be very useful in the domain of emergency management. An important task in emergency management is rapid crisis mapping, which aims to produce timely and reliable maps of affected areas. During an emergency, the volume of emergency-related posts is typically large, but only a small fraction is relevant and help rapid mapping effectively. Furthermore, posts are not useful for mapping purposes unless they are correctly geolocated and, on average, less than 2% of posts are natively georeferenced. This paper presents an algorithm, called CIME, that aims to identify and geolocate emergency-related posts that are relevant for mapping purposes. While native geocoordinates are most often missing, many posts contain geographical references in their metadata, such as texts or links that can be used by CIME to filter and geolocate information. In addition, social media creates a social network and each post can be enhanced with indirect information from the post’s network of relationships with other posts (for example, a retweet can be associated with other geographical references which are useful to geolocate the original tweet). To exploit all this information, CIME uses the concept of context, defined as the information characterizing a post both directly (the post’s metadata) and indirectly (the post’s network of relationships). The algorithm was evaluated on a recent major emergency event demonstrating better performance with respect to the state of the art in terms of total number of geolocated posts, geolocation accuracy and relevance for rapid mapping.

scholarly journals Context-aware Frame-Semantic Role Labeling

2015 ◽  
Vol 3 ◽  
pp. 449-460 ◽  
Author(s):  
Michael Roth ◽  
Mirella Lapata
Keyword(s):  
Social Network ◽  
Question Answering ◽  
State Of The Art ◽  
Semantic Role ◽  
Context Aware ◽  
Semantic Role Labeling ◽  
Current State ◽  
Sentence Level ◽  
Small Set ◽  
Labeling System

Frame semantic representations have been useful in several applications ranging from text-to-scene generation, to question answering and social network analysis. Predicting such representations from raw text is, however, a challenging task and corresponding models are typically only trained on a small set of sentence-level annotations. In this paper, we present a semantic role labeling system that takes into account sentence and discourse context. We introduce several new features which we motivate based on linguistic insights and experimentally demonstrate that they lead to significant improvements over the current state-of-the-art in FrameNet-based semantic role labeling.

scholarly journals Enhancement of Text Analysis Using Context-Aware Normalization of Social Media Informal Text

2021 ◽  
Vol 11 (17) ◽  
pp. 8172
Author(s):  
Jebran Khan ◽  
Sungchang Lee
Keyword(s):  
Social Media ◽  
Text Analysis ◽  
State Of The Art ◽  
Context Aware ◽  
Intended Meaning ◽  
Text Data ◽  
Recall Accuracy ◽  
Wide Range ◽  
Textual Data ◽  
Noisy Text

We proposed an application and data variations-independent, generic social media Textual Variations Handler (TVH) to deal with a wide range of noise in textual data generated in various social media (SM) applications for enhanced text analysis. The aim is to build an effective hybrid normalization technique that ensures the use of useful information of the noisy text in its intended form instead of filtering them out to analyze SM text better. The proposed TVH performs context-aware text normalization based on intended meaning to avoid the wrong word substitution. We integrate the TVH with state-of-the-art (SOTA) deep-learning-based text analysis methods to enhance their performance for noisy SM text data. The proposed scheme shows promising improvement in the text analysis of informal SM text in terms of precision, recall, accuracy, and F1-score in simulation.

scholarly journals Unsupervised Detection of Sub-Events in Large Scale Disasters

2020 ◽  
Vol 34 (01) ◽  
pp. 354-361 ◽  
Author(s):  
Chidubem Arachie ◽  
Manas Gaur ◽  
Sam Anzaroot ◽  
William Groves ◽  
Ke Zhang ◽  
...  
Keyword(s):  
Social Media ◽  
Large Scale ◽  
State Of The Art ◽  
Qualitative Evaluation ◽  
Irrelevant Information ◽  
Data Sets ◽  
Emergency Responders ◽  
Learning Framework ◽  
Emergency Event ◽  
2015 Nepal Earthquake

Social media plays a major role during and after major natural disasters (e.g., hurricanes, large-scale fires, etc.), as people “on the ground” post useful information on what is actually happening. Given the large amounts of posts, a major challenge is identifying the information that is useful and actionable. Emergency responders are largely interested in finding out what events are taking place so they can properly plan and deploy resources. In this paper we address the problem of automatically identifying important sub-events (within a large-scale emergency “event”, such as a hurricane). In particular, we present a novel, unsupervised learning framework to detect sub-events in Tweets for retrospective crisis analysis. We first extract noun-verb pairs and phrases from raw tweets as sub-event candidates. Then, we learn a semantic embedding of extracted noun-verb pairs and phrases, and rank them against a crisis-specific ontology. We filter out noisy and irrelevant information then cluster the noun-verb pairs and phrases so that the top-ranked ones describe the most important sub-events. Through quantitative experiments on two large crisis data sets (Hurricane Harvey and the 2015 Nepal Earthquake), we demonstrate the effectiveness of our approach over the state-of-the-art. Our qualitative evaluation shows better performance compared to our baseline.

scholarly journals Rumour prevention in social networks with layer 2 blockchains

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Subhasis Thakur ◽  
John G. Breslin
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Social Network ◽  
State Of The Art ◽  
Privacy Preserving ◽  
The State ◽  
Media Content ◽  
The Social ◽  
Layer 2 ◽  
Art Methods

AbstractSocial bots can cause social, political, and economical disruptions by spreading rumours. The state-of-the-art methods to prevent social bots from spreading rumours are centralised and such solutions may not be accepted by users who may not trust a centralised solution being biased. In this paper, we developed a decentralised method to prevent social bots. In this solution, the users of a social network create a secure and privacy-preserving decentralised social network and may accept social media content if it is sent by its neighbour in the decentralised social network. As users only choose their trustworthy neighbours from the social network to be part of its neighbourhood in the decentralised social network, it prevents the social bots to influence a user to accept and share a rumour. We prove that the proposed solution can significantly reduce the number of users who are share rumour.

scholarly journals The Role of Social Networks in Emergency Management

2019 ◽  
Vol 10 (4) ◽  
pp. 1346
Author(s):  
Alexey SMIRNOV ◽  
Natalia BELOZEROVA ◽  
Natalia ALANICHEVA ◽  
Irina TRUBNIKOVA ◽  
Yuriy MOTORYGIN
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Social Network ◽  
Emergency Management ◽  
Information Dissemination ◽  
The Social ◽  
The Russian Federation ◽  
Definition Of ◽  
The Impact

The article is focused on the impact of social networks which are becoming increasingly important for information dissemination, user-centered response and efficient coordination between residents and governmental departments at all stages of a disaster. We intend to examine the definition of ‘social network», the most important types of emergency domain-related social media, their functions and benefits. We also propose to investigate the recent activities of the Ministry of Emergencies of the Russian Federation in the sphere of emergency prevention and risk reduction via accessing the social networks pages and assessing their effectiveness.

Emotive Trending And Tracking of Tweets

2017 ◽  
Vol 7 (11) ◽  
pp. 25
Author(s):  
Sanjay Chhataru Gupta
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Social Network ◽  
The Social ◽  
The World ◽  
Complex Events

Popularity of the social media and the amount of importance given by an individual to social media has significantly increased in last few years. As more and more people become part of the social networks like Twitter, Facebook, information which flows through the social network, can potentially give us good understanding about what is happening around in our locality, state, nation or even in the world. The conceptual motive behind the project is to develop a system which analyses about a topic searched on Twitter. It is designed to assist Information Analysts in understanding and exploring complex events as they unfold in the world. The system tracks changes in emotions over events, signalling possible flashpoints or abatement. For each trending topic, the system also shows a sentiment graph showing how positive and negative sentiments are trending as the topic is getting trended.

ANALISIS PERINGKAT TOP BRAND OJEK ONLINE MENGGUNAKAN JEJARING SOSIAL PERCAKAPAN TWITTER

2019 ◽  
Vol 24 (2) ◽  
pp. 88-104
Author(s):  
Ilham Aminudin ◽  
Dyah Anggraini
Keyword(s):  
Social Media ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Path Length ◽  
Average Path Length ◽  
Average Degree ◽  
Clustering Coefficient ◽  
R Programming

Banyak bisnis mulai muncul dengan melibatkan pengembangan teknologi internet. Salah satunya adalah bisnis di aplikasi berbasis penyedia layanan di bidang moda transportasi berbasis online yang ternyata dapat memberikan solusi dan menjawab berbagai kekhawatiran publik tentang layanan transportasi umum. Kemacetan lalu lintas di kota-kota besar dan ketegangan publik dengan keamanan transportasi umum diselesaikan dengan adanya aplikasi transportasi online seperti Grab dan Gojek yang memberikan kemudahan dan kenyamanan bagi penggunanya Penelitian ini dilakukan untuk menganalisa keaktifan percakapan brand jasa transportasi online di jejaring sosial Twitter berdasarkan properti jaringan. Penelitian dilakukan dengan dengan mengambil data dari percakapan pengguna di social media Twitter dengan cara crawling menggunakan Bahasa pemrograman R programming dan software R Studio dan pembuatan model jaringan dengan software Gephy. Setelah itu data dianalisis menggunakan metode social network analysis yang terdiri berdasarkan properti jaringan yaitu size, density, modularity, diameter, average degree, average path length, dan clustering coefficient dan nantinya hasil analisis akan dibandingkan dari setiap properti jaringan kedua brand jasa transportasi Online dan ditentukan strategi dalam meningkatkan dan mempertahankan keaktifan serta tingkat kehadiran brand jasa transportasi online, Grab dan Gojek.

New Democracy in Digital Era through Social Media and News Online

Humaniora ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 13
Author(s):  
Abitassha Az Zahra ◽  
Eko Priyo Purnomo ◽  
Aulia Nur Kasiwi
Keyword(s):  
Social Media ◽  
Social Network ◽  
Communication Networks ◽  
Social Communication ◽  
Development Policy ◽  
Online News ◽  
Digital Era ◽  
The Social ◽  
New Democracy

The research aimed to explain the pattern of social communication on the issue of rejection of the PLTU Batang development policy. It used data on Twitter accounts involved in the rejection of the PLTU Batang development policy. In analyzing existing data, qualitative methods and social analysis networks were used. To see social networks in the rejection of the PLTU Batang development policy, the research used the NodeXL application to find out the patterns of social communication networks in #TolakPLTUBatang. From the results, it can be seen that in the dissemination of social networking information, the @praditya_wibby account is the most central account in the social network and has a strong influence on the social network. The @praditya_wibby account has a role in moving the community through Twitter to make a critical social movement. This means that in the current digital era, democracy enters a new form through the movement of public opinion delivery through social media. Besides, by encouraging the role of online news, the distribution of information becomes faster to form new perceptions of an issue. This is evident from the correlation network where the @praditya_wibby account has correlations with several compass online media accounts, tirto.id, okezonenews, vice, antaranews, BBCIndonesia, and CNN Indonesia.

Measuring Relevant Information in Health Social Network Conversations and Clinical Diagnosis Cases (Preprint)

2018 ◽  
Author(s):  
Albert Moreira ◽  
Raul Alonso-Calvo ◽  
Alberto Muñoz ◽  
Jose Crespo
Keyword(s):  
Social Media ◽  
Social Network ◽  
Relevant Information ◽  
Online Discussions ◽  
Biomedical Knowledge ◽  
Expert Opinions ◽  
Health Related ◽  
Health Social Network ◽  
Discussion System ◽  
Clinical Cases

BACKGROUND Internet and Social media is an enormous source of information. Health Social Networks and online collaborative environments enable users to create shared content that afterwards can be discussed. While social media discussions for health related matters constitute a potential source of knowledge, characterizing the relevance of participations from different users is a challenging task. OBJECTIVE The aim of this paper is to present a methodology designed for quantifying relevant information provided by different participants in clinical online discussions. METHODS A set of key indicators for different aspects of clinical conversations and specific clinical contributions within a discussion have been defined. These indicators make use of biomedical knowledge extraction based on standard terminologies and ontologies. These indicators allow measuring the relevance of information of each participant of the clinical conversation. RESULTS Proposed indicators have been applied to two discussions extracted from PatientsLikeMe, as well as to two real clinical cases from the Sanar collaborative discussion system. Results obtained from indicators in the tested cases have been compared with clinical expert opinions to check indicators validity. CONCLUSIONS The methodology has been successfully used for describing participant interactions in real clinical cases belonging to a collaborative clinical case discussion tool and from a conversation from a Health Social Network.

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