T-CREo: A Twitter Credibility Analysis Framework

Cardinale, Yudith; Dongo, Irvin; Robayo, German; Cabeza, David; Aguilera, Ana; Medina, Sergio A. Ordóñez

doi:10.1109/access.2021.3060623

Cited by 13 publications

(12 citation statements)

References 29 publications

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“…However, all of them are Twitter API dependent, which is now restricted, then most of them are no longer available. Some works combine both data extraction method (Dongo et al, 2019;Cardinale et al, 2021;Shao et al, 2016;Liu et al, 2015;Tan, 2017), by using Twitter API the most of time, but when the API limitations impede the extraction of some data, then Web scraping technique is used. The limitations concern to the limited timeline, URLs access, etc.…”

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

Self Cite

View full text Add to dashboard Cite

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“… 2021 ) and truthfulness: This area focuses on the automatic verification of false accounts, identification of sources of spam as well as identifying the truthfulness of information (Cardinale et al. 2021 ) or identifying privacy violation issues. Opinion mining: A process by which we try to obtain relevant information from texts published on the web (Yousaf et al.…”

Section: The Social Media Mining Problemmentioning

confidence: 99%

Section: The Social Media Mining Problemmentioning

confidence: 99%

A survey on the use of association rules mining techniques in textual social media

2022

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The incursion of social media in our lives has been much accentuated in the last decade. This has led to a multiplication of data mining tools aimed at obtaining knowledge from these data sources. One of the greatest challenges in this area is to be able to obtain this knowledge without the need for training processes, which requires structured information and pre-labelled datasets. This is where unsupervised data mining techniques come in. These techniques can obtain value from these unstructured and unlabelled data, providing very interesting solutions to enhance the decision-making process. In this paper, we first address the problem of social media mining, as well as the need for unsupervised techniques, in particular association rules, for its treatment. We follow with a broad overview of the applications of association rules in the domain of social media mining, specifically, their application to the problems of mining textual entities, such as tweets. We also focus on the strengths and weaknesses of using association rules for solving different tasks in textual social media. Finally, the paper provides a perspective overview of the challenges that association rules must face in the next decade within the field of social media mining.

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“…In this context, we pretend to combine the insights about topic detection and hashtag usage to ameliorate a Twitter credibility model. In previous works, we proposed a credibility model [22] and a framework called T-CREo [41] to evaluate Twitter credibility. Our previous model considers text, user, and social credibility measures to calculate the overall credibility level of tweets.…”

Section: Introductionmentioning

confidence: 99%

Credibility Analysis on Twitter Considering Topic Detection

et al. 2022

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Twitter is one of the most popular sources of information available on the internet. Thus, many studies have proposed tools and models to analyze the credibility of the information shared. The credibility analysis on Twitter is generally supported by measures that consider the text, the user, and the social impact of text and user. More recently, identifying the topic of tweets is becoming an interesting aspect for many applications that analyze Twitter as a source of information, for example, to detect trends, to filter or classify tweets, to identify fake news, or even to measure a tweet’s credibility. In most of these cases, the hashtags represent important elements to consider to identify the topics. In a previous work, we presented a credibility model based on text, user, and social credibility measures, and a framework called T-CREo, implemented as an extension of Google Chrome. In this paper, we propose an extension of our previous credibility model by integrating the detection of the topic in the tweet and calculating the topic credibility measure by considering hashtags. To do so, we evaluate and compare different topic detection algorithms, to finally integrate in our framework T-CREo, the one with better results. To evaluate the performance improvement of our extended credibility model and show the impact of hashtags, we performed experiments in the context of fake news detection using the PHEME dataset. Results demonstrate an improvement in our extended credibility model with respect to the original one, with up to 3.04% F1 score when applying our approach to the whole PHEME dataset and up to 9.60% F1 score when only considering tweets that contain hashtags from PHEME dataset, demonstrating the impact of hashtags in the topic detection process.

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T-CREo: A Twitter Credibility Analysis Framework

Cited by 13 publications

References 29 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

A survey on the use of association rules mining techniques in textual social media

Credibility Analysis on Twitter Considering Topic Detection

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