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Wikipedia's inclusive editorial policy permits unrestricted participation, enabling individuals to contribute and disseminate their expertise while drawing upon a multitude of external sources. News media outlets constitute nearly one-third of all citations within Wikipedia. However, embracing such a radically open approach also poses the challenge of the potential introduction of biased content or viewpoints into Wikipedia. The authors conduct an investigation into the integrity of knowledge within Wikipedia, focusing on the dimensions of source political polarization and trustworthiness. Specifically, the authors delve into the conceivable presence of political polarization within the news media citations on Wikipedia, identify the factors that may influence such polarization within the Wikipedia ecosystem and scrutinize the correlation between political polarization in news media sources and the factual reliability of Wikipedia's content.

The authors conduct a descriptive and regression analysis, relying on Wikipedia Citations, a large-scale open dataset of nearly 30 million citations from English Wikipedia. Additionally, this dataset has been augmented with information obtained from the Media Bias Monitor (MBM) and the Media Bias Fact Check (MBFC).

The authors find a moderate yet significant liberal bias in the choice of news media sources across Wikipedia. Furthermore, the authors show that this effect persists when accounting for the factual reliability of the news media.

The results contribute to Wikipedia’s knowledge integrity agenda in suggesting that a systematic effort would help to better map potential biases in Wikipedia and find means to strengthen its neutral point of view policy.

Text classification is a widely accepted and adopted technique in organizations to mine and analyze unstructured and semi-structured data. With advancement of technological computing, deep learning has become more popular among academicians and professionals to perform mining and analytical operations. In this work, the authors study the research carried out in field of text classification using deep learning techniques to identify gaps and opportunities for doing research.

The authors adopted bibliometric-based approach in conjunction with visualization techniques to uncover new insights and findings. The authors collected data of two decades from Scopus global database to perform this study. The authors discuss business applications of deep learning techniques for text classification.

The study provides overview of various publication sources in field of text classification and deep learning together. The study also presents list of prominent authors and their countries working in this field. The authors also presented list of most cited articles based on citations and country of research. Various visualization techniques such as word cloud, network diagram and thematic map were used to identify collaboration network.

The study performed in this paper helped to understand research gaps that is original contribution to body of literature. To best of the authors' knowledge, in-depth study in the field of text classification and deep learning has not been performed in detail. The study provides high value to scholars and professionals by providing them opportunities of research in this area.

This review article is focused on the following research questions: RQ1: What are the methods used by authors to collect data in order to evaluate one's profile? RQ2: What are the classification algorithms and ranking metrics used to give suggestions to users? RQ3: How effective are these algorithms and metrics identified in RQ2?

There are four major systematic review phases being carried out in this survey, namely the formulation of research questions, conducting the review, which includes the selection of articles and appraising evidence quality, data extraction and narrative data synthesis.

Collecting from primary sources is more personalized and relevant. Embedded skill sets that have a considerable impact on one’s career aspirations could be mined from secondary sources. A hybrid recommender system helped mitigate the limitations of both. The effectiveness of the models depends not only rely on the filtering techniques used but also on the metrics used to measure similarity and the frequency of words or phrases used in a document.

The study benefits internship program coordinators of a university aiming to develop a recommender or matching system platform for their students. The content of the study may shed a light on how university decision-makers can explore options on what are the techniques or algorithms to be integrated. One of the advantages of internship or industrial training programs is that they would help students align them with their career goals. Research studies have discussed other RS filtering techniques apart from the three major filtering techniques.

The outcome of the study, which is a recommendation system to match a student's profile with the knowledge and skills being sought by organizations, may help ease the challenges encountered by both parties. The study benefits internship coordinators of a university who are planning to create a recommendation system, an innovative project to be used in teaching and learning.

Internship programs can help a student grow personally and professionally. A university student looking for internship opportunities can find it a daunting task to undertake, as there is a vast pool of opportunities offered in the market. The confidence levels needed to match their knowledge, skills and career goals with the job descriptions (JDs) could be challenging. The same holds with companies, as finding the right people for the right job is a tough endeavor. The main objective of conducting this study is to identify models implemented in recommendation systems to give and/or rank suggestions given to users.

While surveys regarding recommender systems (RS) exist, there are gaps in the presentation of various data collection methods and the comparison of recommendation filtering techniques used for both primary and secondary sources of data. Most recommendation systems for internship programs are intended for European universities and not much for Southeast Asia. There are also a limited number of comparative studies or systematic review articles related to recommendation systems for internship programs offered in an Southeast Asian landscape. Systematic reviews on the usability of the proposed recommendation systems are also limited. The study presents reviews of articles, from data collection and techniques used to the usability of the proposed recommendation systems, which were presented in the articles being studied.

This study was designed to discover the readiness of the higher education commission (HEC)-recognized journals of Pakistan in terms of human, financial and technological resources, technical expertise, institutional support, availability of open access (OA) policy, availability of guidance and training, willingness, motivation and so on for OA journal publishing and to expose the challenges in OA journal publishing.

A quantitative research approach was used and a structured questionnaire was developed to meet the objectives of this study. A survey method was used to collect data from the editors of all 329 HEC-recognized journals in Pakistan.

The respondents of all the HEC-recognized journals of Pakistan are neutral and are not of the view that they have sufficient financial, human, technological/infrastructural resources and technical expertise to continue/initiate an OA journal publishing. ‘No academic reward’; and ‘no monetary reward for the editorial staff’ are both enormous challenges for OA journal publishing. The perceived challenges of OA have a negative impact on readiness for OA publishing. The readiness level of the respondents of the OA journals is higher as compared to the readiness level of the respondents of non-OA journals.

This study covered the lists of HEC-recognized journals of 2019. More studies may be conducted based on updated lists of HEC-recognized journals. Qualitative studies may also be conducted to discover the readiness of the HEC-recognized journals of Pakistan for OA journal publishing.

This study is the first comprehensive study on this phenomenon and is an effort to fill this gap to invigorate scholarly literature. It may attract the attention of policymakers, funding bodies, parent institutions of the journals and the HEC regarding the readiness of journals in terms of financial, human, technological/infrastructural resources, technical expertise of the journals and challenges of journals to prompt the OA journal publishing paradigm.

This paper aims to discern the potential dimensions amid the duality of heritage tourism and peace. Reflecting on the phases of destruction and rebuilding of Seated Buddha of Jahanabad, this study used the content analysis of 40 news sources, to unravel the resultant avenues of heritage tourism and peace.

Following the qualitative research strategy, the interface of NVivo 12 has been used to transcribe the textual and visual content of media news. The media news aired on the incident of destruction phase in 2007, and rebuilding drive in 2012–2016 were the two sets of collected data. A hierarchy of thematic analysis was adopted to identify nodes, subthemes and themes.

Findings of this study highlighted six themes: peaceful imagery, PI; heritage dissonance, HD, vs interfaith harmony, IH; peace allegory through restoration, PAR; precursor of heritage sustainability, PHS; community heritage consonance, CHC; and heritage touristic valuation, HTV.

This study lacks statistical data of the quantitative research domain. Aimed at a single heritage site, it analyzed limited number of news sources.

This study offers implications for industrial, theoretical, managerial and governmental stakeholders in their respective domains. Moreover, it also provides takeouts for common readers.

This study contends a significant research issue and analyzes the destruction and rebuilding of a heritage site in a developing country. Primarily in the sociogeographic context of the research issue, the resultant dimensions are novel and demanding.