Data Extraction from Online Social Networks Using Application Programming Interface in a Multi Agent System Approach

Abdulrahman, Ruqayya; Neagu, Daniel; Holton, D. R. W.; Ridley, Mick; Lan, Y.

doi:10.1007/978-3-642-41776-4_4

Cited by 3 publications

(2 citation statements)

References 28 publications

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“…For instance, Twitter REST APIs can be employed to collect data from the identified Twitter accounts of the communities that can return up to 3200 of users most recent Tweets [26]. In addition, the Online Social Network Retrieval Systems (OSNRS) is a tool developed to decentralize the retrieving system that uses Multi Agent System (MAS) technology which can monitor profiles continuously and involves replacing the parser with the Application Programming Interface (API) tool [27]. In this work, information on job advertisements are obtained from the Twitter and Facebook page of Jobs Perak to observe others' opinions and discussions on the job vacancy.…”

Section: Online Job Searchingmentioning

confidence: 99%

Visualization of job availability based on text analytics localization approach

Zamani

Kamaruddin

Wahab

et al. 2019

IJEECS

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<span>Rate of employment is a strong indicator of economic stability of a country. It relates to the number of volumes of produced products and services. If the unemployment rate is high, the amount of gross domestic product (GDP) of a country may be declined. One of the main factors that contributes to low rate of employment is the mismatch between job seeker and the requirement of the job applied. This is due to the limited analysis performed on the relevant information on job advertisement; such as, skills, responsibilities of the job, location and expectation of the employers. The obscure job descriptions provided in the advertisement may result in application of unsuitable candidates that can cause rejection of the candidate and the potential employer may take a long time to filter and evaluate the applications. A system that is able to provide relevant information in a simple and catchy way is needed to simplify the task of job searching. In this paper we proposed a text analytics technique to extract users’ comments from social media such as Twitter and Facebook on job advertisement. The result is then displayed in a geotagged map that can reveal the density of job availability based on geographical location. The job seekers can easily observe and select their desired job location. The initial system shows potential of the inclusion of the proposed approach in job advertisement websites. In comparison to other job searching websites, this system can provide additional information on public view about the advertised job obtained from the social media text analytics. With this additional information, jobseekers have more confidence in job selection and allows employers to receive more suitable candidates for the available positions. It is hoped that the proposed system can tailor the job advertisements to the need of the jobseeker and making the job application more relevant hence reducing the potential employers’ processing time.</span>

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Section: Online Job Searchingmentioning

confidence: 99%

Visualization of job availability based on text analytics localization approach

Zamani

Kamaruddin

Wahab

et al. 2019

IJEECS

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“…Tracking of social media users for information retrieval has been studied using mainly three ways: social networks' interface crawling through public and false profiles and phishing techniques [4], social networks applications such as Netvizz [10], as well as social networks Application Programming Interfaces (APIs) [1]. These techniques have provided a direct access to an important number of users.…”

Section: Related Workmentioning

confidence: 99%

MASIR: A Multi-agent System for Real-Time Information Retrieval from Microblogs During Unexpected Events

Bizid

Boursier

Morcos

et al. 2015

Agent and Multi-Agent Systems: Technologies and Applications

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Abstract. Microblogs have proved their potential to attract people from all over the world to express voluntarily what is happening around them during unexpected events. However, retrieving relevant information from the huge amount of data shared in real time in these microblogs remain complex. This paper proposes a new system named MASIR for real-time information retrieval from microblogs during unexpected events. MASIR is based on a decentralized and collaborative multi-agent approach analyzing the profiles of users interested in a given event in order to detect the most prominent ones that have to be tracked in real time. Real time monitoring of these users enables a direct access to valuable fresh information. Our experiments shows that MASIR simplifies the real-time detection and tracking of the most prominent users by exploring both the old and fresh information shared during the event and outperforms the standard centrality measures by using a time-sensitive ranking model.

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A Framework for the Efficient Collection of Big Data from Online Social Networks

Consolo

Petrillo

2014

2014 International Conference on Intelligent Networking and Collaborative Systems

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Data Extraction from Online Social Networks Using Application Programming Interface in a Multi Agent System Approach

Cited by 3 publications

References 28 publications

Visualization of job availability based on text analytics localization approach

Visualization of job availability based on text analytics localization approach

MASIR: A Multi-agent System for Real-Time Information Retrieval from Microblogs During Unexpected Events

A Framework for the Efficient Collection of Big Data from Online Social Networks

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