A Distributed Approach for Mining Moroccan Hashtags using Twitter Platform

Abdouli, Abdeljalil El; Hassouni, Larbi; Anoun, Houda

doi:10.1145/3320326.3320407

Cited by 5 publications

(3 citation statements)

References 13 publications

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“…The present study examined the functions and effects of a few important factors that contribute to the misinterpretation of hashtags, such as lack of vocabulary in every type of a hashtag, writing style (case-insensitivity) and lengths of hashtags. It also affirms the results of a recent study of El Abdouli et al (2019) on Moroccan hashtags on Twitter which explains that content and style cause misinterpretation. The present study further expands on the Moroccan hashtag study by exploring a few additional factors that cause misinterpretation on Facebook in Pakistan and the reasons for such misinterpretation.…”

Section: Discussionsupporting

confidence: 87%

“…The role of hashtags and English language learners has also been researched in several studies, but, as per researchers' knowledge, it is limited to Twitter hashtags. The English hashtags on Twitter written by the speakers of other languages include “spelling errors and abbreviations and do not contain delimiters between words,” which may be the reasons for misinterpretation (El Abdouli et al , 2019). Although a recent study of El Abdouli et al (2019) on Moroccan hashtags on Twitter explains a few factors that cause misinterpretation, many factors that are observed on Facebook in Pakistan remain undiscussed.…”

Section: Review Of Literaturementioning

confidence: 99%

“…The English hashtags on Twitter written by the speakers of other languages include “spelling errors and abbreviations and do not contain delimiters between words,” which may be the reasons for misinterpretation (El Abdouli et al , 2019). Although a recent study of El Abdouli et al (2019) on Moroccan hashtags on Twitter explains a few factors that cause misinterpretation, many factors that are observed on Facebook in Pakistan remain undiscussed. Miller, Morgan and Koronkiewicz (2019) highlighted the role of these two SNSs (Twitter and Facebook), but different types of hashtags and the reasons for misinterpretation on Facebook were not explored in their study too.…”

Section: Review Of Literaturementioning

confidence: 99%

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Digital media users and Facebook hashtags' misinterpretations

Tariq

Sarfraz

Abbas

2020

OIR

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PurposeThis paper examines reasons of pragmatic functions' misinterpretation of three types of Facebook hashtags: long, short and multiple mixed hashtags.Design/methodology/approachFocus group interviews of 15 English language learners, who are also active users of Facebook and hashtags, are conducted. Thematic analysis is performed through the software Nvivo for arriving at reliable findings.FindingsThe findings show that unknown vocabulary in contents and lowercasing in long and short hashtags are major factors that cause misinterpretation. Also, the symbol of underscore and appropriate usage of upper and lowercasing of alphabets should be adopted in hashtag writing for the improvement in communication and successful conveyance of the intended meaning.Originality/valueThis paper contributes to the online hashtag writing style by finding reasons for the misinterpretations of different types of hashtags. Hashtags have been developed for adequate communication (Livingstone, 2012), but in Pakistan, hashtags are practiced as a “fashionable” trend and thus result in misinterpretation and inadequate communication among readers even of the same background (Tariq and Sarfraz, 2018). Moreover, this study focuses on the trend of hashtagging that is common among university students and particularly second-language learners because they are active users of Facebook and adopt the latest trends quickly.

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Section: Discussionsupporting

confidence: 87%

Section: Review Of Literaturementioning

confidence: 99%

Section: Review Of Literaturementioning

confidence: 99%

See 1 more Smart Citation

Digital media users and Facebook hashtags' misinterpretations

Tariq

Sarfraz

Abbas

2020

OIR

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Sentiment Analysis of Real-Time Health Care Twitter Data Using Hadoop Ecosystem

Hussain

Ghawi

2023

Lecture Notes in Networks and Systems

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HTwitt: a hadoop-based platform for analysis and visualization of streaming Twitter data

Demirbaga

2021

Neural Comput & Applic

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Twitter produces a massive amount of data due to its popularity that is one of the reasons underlying big data problems. One of those problems is the classification of tweets due to use of sophisticated and complex language, which makes the current tools insufficient. We present our framework HTwitt, built on top of the Hadoop ecosystem, which consists of a MapReduce algorithm and a set of machine learning techniques embedded within a big data analytics platform to efficiently address the following problems: (1) traditional data processing techniques are inadequate to handle big data; (2) data preprocessing needs substantial manual effort; (3) domain knowledge is required before the classification; (4) semantic explanation is ignored. In this work, these challenges are overcome by using different algorithms combined with a Naïve Bayes classifier to ensure reliability and highly precise recommendations in virtualization and cloud environments. These features make HTwitt different from others in terms of having an effective and practical design for text classification in big data analytics. The main contribution of the paper is to propose a framework for building landslide early warning systems by pinpointing useful tweets and visualizing them along with the processed information. We demonstrate the results of the experiments which quantify the levels of overfitting in the training stage of the model using different sizes of real-world datasets in machine learning phases. Our results demonstrate that the proposed system provides high-quality results with a score of nearly 95% and meets the requirement of a Hadoop-based classification system.

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A Distributed Approach for Mining Moroccan Hashtags using Twitter Platform

Cited by 5 publications

References 13 publications

Digital media users and Facebook hashtags' misinterpretations

Digital media users and Facebook hashtags' misinterpretations

Sentiment Analysis of Real-Time Health Care Twitter Data Using Hadoop Ecosystem

HTwitt: a hadoop-based platform for analysis and visualization of streaming Twitter data

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