Factors That Motivates Fake News Sharing Among Social Media Users: A Case of an Emerging Economy

False news is not new but it is becoming more common, it has become a serious issue as a result of social media's expansion, which has permitted individuals to engage and share thoughts. The primary aim of the research is to investigate those factors that influence fake news sharing on social media in an emerging economy. It is necessary to target emerging economy as fake news create a massive panic in such challenging economies that ultimately affect various sectors. For this purpose, Uses and Gratification theory (U&G) is used. In this research, the independent variable is False information and dependent variables are Altruism, Information sharing, Socialization, Entertainment and Pass time. In this research, quantitative method is used to investigate concepts to find relationships between variables and forecast results. In this research, the correlation research approach is used. A survey was conducted with local students via questionnaire (n=150). For data analysis, SPSS and smart PLS-SEM is used in this research. According to the findings of the study, altruism is the foremost imperative indicator of fake news distribution among Pakistanis. We have come to the conclusion that Altruism, Entertainment and Pass the Time foresee the spread of bogus news positively while Sharing of Information and Socialization impacts negatively. We recommend that online platforms users must verify the veracity of the information they have encounter and then post on social media websites

Lexicon reduction for Urdu/Arabic script based character recognition: A multilingual OCR

Arabic script character recognition is challenging task due to complexity of the script and huge number of ligatures. We present a method for the development of multilingual Arabic script OCR (Optical Character Recognition) and lexicon reduction for Arabic Script and its derivative languages. The objective of the proposed method is to overcome the large dataset Urdu and similar scripts by using GCT (Ghost Character Theory) concept. Arabic and its sibling script languages share the similar character dataset i.e. the character set are difference in diacritic and writing styles like Naskh or Nasta\u27liq. Based on the proposed method, the lexicon for Arabic and Arabic script based languages can be minimized approximately up to 20 times. The proposed multilingual Arabic script OCR approach have been evaluated for online Arabic and its derivative language like Urdu using BPNN. The result showed that proposed method helps to not only the reduction of lexicon but also helps to develop the Multilanguage character recognition system for Arabic Script

A hybrid approach for NER system for Scarce Resourced Language-URDU: Integrating n-gram with rules and gazetteers

We present a hybrid NER (Name Entity Recognition) system for Urdu script by integration of n-gram model (unigram and bigram), rules and gazetteers. We used prefix and suffix characters for rule construction instead of first name and last name lists or potential terms on the output list that is produced by n-gram model. Evaluation of the system is performed on two corpora, the IJCNLP NE (Named Entity) corpus and CRL NE corpus in Urdu text. The system achieved 92.65 and 87.6% using hybrid unigram and 92.47 and 86.83% using hybrid bigram on IJCNLP NE corpus and CRL NE corpus, respectively