Multilingual author profiling on Facebook

Fatima, Mehwish; Hasan, K. Z.; Anwar, Saba; Nawab, Rao Muhammad Adeel

doi:10.1016/j.ipm.2017.03.005

Cited by 46 publications

(20 citation statements)

References 16 publications

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“…Juola and Mikros [25] used entropy, repeat rate of words, R index, and linguistic and Twitter-specific extra-linguistic variables to identify fourteen bilingual Twitter users. Fatima et al [27] developed a multilingual (English and Roman Urdu, i.e., Urdu language written with the Roman script) corpus for authorship profiling task on Facebook. They suggsted that the existing stylometric features like n-grams, lexical, and content-based features can also be used on the multilingual corpus.…”

Section: Related Workmentioning

confidence: 99%

Agree-to-Disagree (A2D): A Deep Learning-Based Framework for Authorship Discrimination Task in Corpus-Specificity Free Manner

et al. 2020

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Authorship discrimination is the task of detecting whether two writings are authored by the same person. From literature study to forensic analysis, the authorship discrimination makes a significant contribution in differentiating authorship. In this work, we propose Agree-to-Disagree (A2D), a novel framework for the authorship discrimination task. It is a two-stage deep learning-based framework consisting of an 'Agree' and a 'Disagree' network. At the first stage, it learns the authorship attributes with its Agree network. Subsequently, through its Disagree network, the framework attempts to differentiate the authorship of a new dataset (completely unrelated to the training dataset), a novel use case that has not been systematically considered hitherto in the literature. We show that A2D is not dependent on the datasetspecific prior knowledge and it can learn only from authorship attributes of the dataset to detect whether two different writings are from the same author. We prove that the A2D framework can successfully reveal the authorship with pseudonyms through tasking it with unfolding the pseudonyms of a famous American short story writer Washington Irving. We also apply our framework on a historical topic of ascertaining whether the authorship of the most respected book in Islam (the Holy Quran) can be attributed to the Prophet of Islam. Through the experimental analysis, A2D reveals that the Prophet of Islam is not the author of the Holy Quran, and this result is in perfect alignment with the belief of 1.8 billion Muslims around the globe regarding the authorship of this holy book. INDEX TERMS Authorship discrimination, deep learning, machine learning, natural language processing, neural networks.

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Section: Related Workmentioning

confidence: 99%

Agree-to-Disagree (A2D): A Deep Learning-Based Framework for Authorship Discrimination Task in Corpus-Specificity Free Manner

et al. 2020

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“…The attribute relations association in RTC were resolved in diagram weighting. Social Network User can be represented in graphical model for removing principle face account words [7]. The rugged way in hub method and total closeness is one mode diagram of weight method in RTC.…”

Section: Related Workmentioning

confidence: 99%

A fusion fuzzy model for detecting phony accounts in social networks

Rao

Gyani

Narsimha

2018

IJET

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In online social network’s phony account detection is one of the major task among the ability of genuine user from forged user account. The fundamental objective of detection of phony account framework is to detect fake account and removal technique in Social network user sites. This work concentrates on detection of phony account in which it depends on normal basis framework, transformative Algorithms and fuzzy technique. Initially, the most essential attributes including personal attributes, comparability techniques and various real user review, tweets, or comments are extricated. A direct blend of these attributes demonstrates the significance of each reviews tweets comments etc. To compute closeness measure, a consolidated strategy in view of artiﬁcial honey bee state Algorithm and fuzzy technique are utilized. Second approach is proposed to alter the best weights of the normal user attributes utilizing the social network activities/transaction and inherited Algorithm. Finally, a normal rank rationale framework is utilized to calculate the ﬁnal scoring of normal user activities. The decision making of proposed approach to find phony account are variation with existing techniques user behavioral analysis using data sets and machine learning techniques such as crowdflower_sample and genuine_accounts_sample dataset of facebook and Twitter. The outcomes demonstrate that proposed strategy overcomes the previously mentioned strategies.

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“…As a result, English, as a second language, converged with Urdu and gave birth to the popular dialect called Roman Urdu, which uses English alphabets and Urdu semantics. Roman Urdu is the preferred choice for digital communications (Ali and Mahmood Aslam 2012), social media platforms like Twitter (Javed and Afzal 2013; Javed et al 2014; Afzal and Mehmood 2016), Facebook (Fatima et al 2017) and blogs (Mukund and Srihari 2012; Bilal et al 2016). Roman Urdu has developed as a distinctive communication genre because of the ease and comfort it offers to its users who possess average or below average literacies.…”

Section: Introductionmentioning

confidence: 99%

“…More than half of all Europeans claim to speak at least one other language in addition to their native language (Eurobarometer 2006). The past decade has seen a rapid increase in the usage of internet-based technology that resulted in forming a global, multilingual culture; garnering research interest in multilingual corpora (Calvo et al 2017; Fatima et al 2017; Malmasi and Dras 2017). Critics have also analyzed the shifting patterns of language choices, interactional features of multilingualism and distribution of code-switching models (Bhatt and Bolonyai 2011; Ali and Mahmood Aslam 2012; Eleta and Golbeck 2014; Halim and Maros 2014).…”

Section: Introductionmentioning

confidence: 99%

“…As discussed earlier that benchmark corpora are needed to develop, evaluate, compare and analyze the author profiling methods for various genres and languages. The previous work on author profiling has been limited to the corpora generated from emails (Corney et al 2002), tweets (Rangel et al 2014), hotel reviews (Rangel et al 2014), blogs (Rangel et al 2013, 2014) and other social media platforms like Facebook (Rangel et al 2014; Fatima et al 2017). The existing corpora are predominantly monolingual which were developed for English and other European languages (Corney et al 2002; Glance et al 2005; Rangel et al 2013, 2014).…”

Section: Introductionmentioning

confidence: 99%

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Multilingual SMS-based author profiling: Data and methods

Fatima¹,

Anwar²,

Naveed³

et al. 2018

Nat. Lang. Eng.

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In the recent years, many benchmark author profiling corpora have been developed for various genres including Twitter, social media, blogs, hotel reviews and e-mail, etc. However, no such standard evaluation resource has been developed for Short Messaging Service (SMS), a popular medium of communication, which is very useful for author profiling. The primary aim of this study is to develop a large multilingual (English and Roman Urdu) benchmark SMS-based author profiling corpus. The proposed corpus contains 810 author profiles, wherein each profile consists of an aggregation of SMS messages as a single document of an author, along with seven demographic traits associated with each author profile: gender, age, native language, native city, qualification, occupation and personality type (introvert/extrovert). The secondary aims of this study include the following: (1) annotating the proposed corpus for code-switching annotations at the lexical level (approximately 0.69 million tokens are manually annotated for code-switching) and (2) applying the stylometry-based method (groups of sixty-four features) and the content-based method (twelve features) for gender identification in order to demonstrate how our proposed corpus can be used for the development and evaluation of various author profiling methods. The results show that the content-based character 5-gram feature outperformed all the other features by obtaining the accuracy score of 0.975 andF1score of 0.947 for gender identification while using the entire corpus. Furthermore, our proposed corpora (SMS–AP–18 and code-switched SMS–AP–18) are freely and publicly available for research purpose.

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Multilingual author profiling on Facebook

Cited by 46 publications

References 16 publications

Agree-to-Disagree (A2D): A Deep Learning-Based Framework for Authorship Discrimination Task in Corpus-Specificity Free Manner

Agree-to-Disagree (A2D): A Deep Learning-Based Framework for Authorship Discrimination Task in Corpus-Specificity Free Manner

A fusion fuzzy model for detecting phony accounts in social networks

Multilingual SMS-based author profiling: Data and methods

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