A Model Configuration of Social Media Text Mining for Projecting the Online-Commerce Transaction (Case: Twitter Tweets Scraping)

Kaburuan, Emil Robert; Lindawati, Ang Swat Lin; Surjandy, Surjandy; Siswantini,; Putra, Muhammad Ryanda; Utama, Ditdit Nugeraha

doi:10.1109/citsm47753.2019.8965417

Cited by 5 publications

(5 citation statements)

References 14 publications

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“…Beyond credibility analysis and comparison among data extraction methods, there are works that emphasize in Web scraping as an effective alternative to gather data, such as the studies presented in Glez-Peña et al (2013), to extract biomedical data in real-time; in Kusumasari and Prabowo (2020) to show the pattern of use of Twitter to send warnings and identify crucial needs and responses considering Twitter as a communication channel; Kaburuan et al (2019), to analyze tweets and see the commerce and tax-income potency in purposely Indonesia; and in Dewi et al (2019), to propose a method able to search information, combine and present it in a better way according to user preferences.…”

Section: Data Extraction Methods In Platforms For Credibility Analysismentioning

confidence: 99%

A qualitative and quantitative comparison between Web scraping and API methods for Twitter credibility analysis

Dongo

Cardinale

Aguilera

et al. 2021

IJWIS

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Purpose This paper aims to perform an exhaustive revision of relevant and recent related studies, which reveals that both extraction methods are currently used to analyze credibility on Twitter. Thus, there is clear evidence of the need of having different options to extract different data for this purpose. Nevertheless, none of these studies perform a comparative evaluation of both extraction techniques. Moreover, the authors extend a previous comparison, which uses a recent developed framework that offers both alternates of data extraction and implements a previously proposed credibility model, by adding a qualitative evaluation and a Twitter-Application Programming Interface (API) performance analysis from different locations. Design/methodology/approach As one of the most popular social platforms, Twitter has been the focus of recent research aimed at analyzing the credibility of the shared information. To do so, several proposals use either Twitter API or Web scraping to extract the data to perform the analysis. Qualitative and quantitative evaluations are performed to discover the advantages and disadvantages of both extraction methods. Findings The study demonstrates the differences in terms of accuracy and efficiency of both extraction methods and gives relevance to much more problems related to this area to pursue true transparency and legitimacy of information on the Web. Originality/value Results report that some Twitter attributes cannot be retrieved by Web scraping. Both methods produce identical credibility values when a robust normalization process is applied to the text (i.e. tweet). Moreover, concerning the time performance, Web scraping is faster than Twitter API and it is more flexible in terms of obtaining data; however, Web scraping is very sensitive to website changes. Additionally, the response time of the Twitter API is proportional to the distance from the central server at San Francisco.

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Section: Data Extraction Methods In Platforms For Credibility Analysismentioning

confidence: 99%

A qualitative and quantitative comparison between Web scraping and API methods for Twitter credibility analysis

Dongo

Cardinale

Aguilera

et al. 2021

IJWIS

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“…Besides web scrapers, one of the most widely used resources for doing WDS is Python language. It is particularly used for the analysis of Twitter pages, through a dedicated library-Tweepy [8,9]. Another way of applying Python is through the Text Blob Tool which analyzes the sentiments of tweets [10].…”

Section: Wds: a Big Data Sourcementioning

confidence: 99%

The Value of Web Data Scraping: An Application to TripAdvisor

Barbera

Araújo

Fernandes

2023

BDCC

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Social Media Analytics (SMA) is more and more relevant in today’s market dynamics. However, it is necessary to use it wisely, either in promoting any kind of product/brand, or interacting with customers. This requires its effective understanding and monitoring. One way is through web data scraping (WDS) tools that allow to select sites and platforms to compare them in their performances. They can optimize extraction of big data published on social media. Due to current challenges, a sector that can particularly take advantage of this source is tourism (and its related sectors). This year has the hope of tourism’s revival after a pandemic whose impacts are still affecting several activities. Many traders and entrepreneurs have already used these versatile tools. However, do they really know their potential? The present study highlights the use of WDS to collect data from TripAdvisor’s social pages. Besides comparing competitors’ performance, companies also gain new knowledge of unnoticed preferences/habits. This contributes to more interesting innovations and results for them and for their customers. The approach used here is based on a project for smart tourism consultancy, from the identification of a gap in our region, to aid tourism organizations to enhance their digital presence and business model. Many things can be detected in this big source of unstructured data very quickly and easily without programming. Moreover, exploring code, either to refine the web scraper or connect it with other platforms/apps, can be an object of future research to leverage consumer behavior prediction for more advanced interactions.

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“…The mathematical and fuzzy logic are two main methods exploited here. Correspondingly, [6] constructed a text-mining model to extract Twitter users' tweet that is able to be used to envisage the online commerce behavior and tax-income. Furthermore, [8] constructed a decision making model for improving sustainable supply chain capabilities particularly in textile industry.…”

Section: Related Workmentioning

confidence: 99%

“…[5] operated a social-media in decision support model (DSM) construction for making a strategic decision particularly in solving a problem of waste management in Indonesia. Also [6] performed a study by operating a social-media Twitter to crape a users' tweets to see commerce behavior and predict the tax-income for Indonesia. The model was constructed based on Python programing.…”

Section: Introductionmentioning

confidence: 99%

Social-Media based DSM for Strategic Decision Making: Corruption Case

Utama

2020

IJRTE

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Social-media is a communication media based on internet technology. It is a big virtual place where data and information could be practically mined. Furthermore, the data and information are able to be operated in research purposes. One of them, they could be mined to generate decision parameters. This study operated social media to dig all clues in topic corruption. They then generated to become decision parameters that are ready to be involved in to the model. Decision support model (DSM) was constructed scientifically based on two main methods; fuzzy logic and mathematical model. They were functioned respectively to eliminate a bias characteristic of parameters value and calculate the quality of corruption handling and propose the most objective strategic decision to solve the problem, particularly in Indonesia. The model was able to simulate the decision alternative measurement and propose the best decision of several strategic decision alternatives.

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A Model Configuration of Social Media Text Mining for Projecting the Online-Commerce Transaction (Case: Twitter Tweets Scraping)

Cited by 5 publications

References 14 publications

A qualitative and quantitative comparison between Web scraping and API methods for Twitter credibility analysis

A qualitative and quantitative comparison between Web scraping and API methods for Twitter credibility analysis

The Value of Web Data Scraping: An Application to TripAdvisor

Social-Media based DSM for Strategic Decision Making: Corruption Case

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