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Social media networks like Twitter, Facebook, WhatsApp etc. are most commonly used medium for sharing news, opinions and to stay in touch with peers. Messages on twitter are limited to 140 characters. This led users to create their own novel syntax in tweets to express more in lesser words. Free writing style, use of URLs, markup syntax, inappropriate punctuations, ungrammatical structures, abbreviations etc. makes it harder to mine useful information from them. For each tweet, we can get an explicit time stamp, the name of the user, the social network the user belongs to, or even the GPS coordinates if the tweet is created with a GPS-enabled mobile device. With these features, Twitter is, in nature, a good resource for detecting and analyzing the real time events happening around the world. By using the speed and coverage of Twitter, we can detect events, a sequence of important keywords being talked, in a timely manner which can be used in different applications like natural calamity relief support, earthquake relief support, product launches, suspicious activity detection etc. The keyword detection process from Twitter can be seen as a two step process: detection of keyword in the raw text form (words as posted by the users) and keyword normalization process (reforming the users’ unstructured words in the complete meaningful English language words). In this paper a keyword detection technique based upon the graph, spanning tree and Page Rank algorithm is proposed. A text normalization technique based upon hybrid approach using Levenshtein distance, demetaphone algorithm and dictionary mapping is proposed to work upon the unstructured keywords as produced by the proposed keyword detector. The proposed normalization technique is validated using the standard lexnorm 1.2 dataset. The proposed system is used to detect the keywords from Twiter text being posted at real time. The detected and normalized keywords are further validated from the search engine results at later time for detection of events.

The study explores the perspectives of college students on the pedagogical shift as well as frequent transitions between online and offline learning modes during the COVID-19 pandemic in Kerala, the most literate state in India.

A descriptive cross-sectional study was conducted among 1,366 college students in Kerala during December 2021. A pre-tested questionnaire was sent using Google Forms to students of arts and science colleges. The authors analyzed quantitative data using descriptive statistics and qualitative data using thematic content analysis.

The reported advantages of online learning were increased technical skill, flexibility in study time, effectiveness in bridging the gap of the missed academic period and provision of attending more educational webinars. Students expressed concerns of increased workload, difficulty in concentration due to family circumstances, academic incompetency, uncleared doubts and addiction to mobile phones and social media during the online classes. The main advantages reported for switching to an offline learning mode were enhanced social interaction, effective learning, better concentration and reduced stress. The reported challenges of offline classes were fear of getting the disease, concern of maintaining social distancing and difficulty in wearing masks during the classes. The shift in offline to online learning and vice versa was perceived as a difficult process for the students as it took a considerable time for them to adjust to the switching process of learning.

Students' concerns regarding transition between different learning modes provide important information to educators to better understand and support the needs of students during the pandemic situations.

This study explores the relationship between leverage and earnings management in the context of Palestinian-listed companies, while also investigating whether audit industry specialization influences this relationship.

The data used in this study are extracted from public financial reports of 39 firms listed on Palestine Stock Exchange (PEX), spread across the service, insurance, industry and investment sectors, for the time period 2011–2022. A model is developed to test 4 hypotheses about the relationships between long-term and short-term debts, and earnings management, and then to examine the influence of audit industry specialization on these relationships.

The results depict a significant, negative relationship between long-term debt and earnings management. Whereas the association between short-term debt and earnings management is insignificant. Audit industry specialization is proven to have no influence on the relationships between the independent and the dependent variables. Results are robust for firms that changed their accounting policies and using different audit industry specialization proxies.

The association between leverage and earnings management is a significant research topic, given that previous research identifies credit ratings and debt covenant violations as key factors which motivate earnings management. This paper fills a substantial research gap by examining the relationship between the two variables in the context of Palestinian-listed firms, while emphasizing the distinction between long-term and short-term debts. It also highlights key relationships that have been neglected in this particular context, which adds to the body of literature. Furthermore, the research's findings provide a solid information base that is of great interest to accounting and auditing experts and that may be seriously evaluated to support and advance the PEX sector.

This case study aims to analyse the different factors that cause a decline in an organisation's performance. It projects data for the prospective case readers to explore the possible approaches for the Chairman-cum-Managing Director (CMD) of Mahanagar Telephone Nigam Limited (MTNL) and Bharat Sanchar Nigam Limited (BSNL) to turnaround both the organisations. Furthermore, the case compels the readers to study the Indian Telecom industry to analyse the competitive behaviour and the consequent actions necessary to survive and thrive amongst their peers. From the theoretical perspective, the case emphasises the recent change observed in the Telecom industry regarding the transition from value-chain to value-network.

The authors collected the case facts and data for the case study from secondary sources like the latest news articles, the CRISIL database, company annual statements, company press releases and government regulatory body web portals.

The case study has identified the issues pertinent in the public sector companies in India, especially in the telecom sector, concerning leadership, pending government financial commitments and a slow-moving attitude towards taking action.

The case study highlights the management problems faced by the CMD of the two public sector telecom companies i.e. BSNL and MTNL.

The current study assesses the contribution of entrepreneurship education (EE) in strengthening entrepreneurial attitudes (EAs) and entrepreneurial intentions (EIs) among engineering graduates in India.

Cross-sectional data were collected through an electronic questionnaire from 340 engineering students. Structural equation modeling was performed for hypothesis testing through SmartPLS4 software.

The findings demonstrated that EE, EA and EI are positively and significantly correlated. However, the moderation effect of gender on EE–EI linkage was found to be insignificant.

The study provides comprehensive insights to understand EE effectiveness on students' EI and further opens the path for future researchers to investigate how the inclusion of other constructs in theory of planned behavior and human capital theory can raise the EI among students. Future research should target a larger sample size comprising students from diverse educational streams.

The findings of this research offer various practical contributions for educational establishments, policymakers and the government in formulating constructive educational interventions that fully trigger the student's EIs.

This study adds to the scarce theoretical examination of EE–EI using the human capital approach in developing countries. In addition, this study is highly relevant to the scarce theoretical and empirical support for investigating the contribution of EE in HEIs in India.

This paper focuses on two examples of constitutional corruption in India where the constitution is used for questionable political reasons by the Bharatiya Janata Party under the leadership of Prime Minister Narendra Modi.

The paper relies on public documents and media reports to analyse Prime Minister Modi's handling of the purchase of Rafale jet fighters from France and the revocation of Articles 370 and 35A which resulted in the division of the State of Jammu and Kashmir.

Constitutional and democratic norms were violated in both cases, but the Supreme Court did not find any irregularities in the sale of the Rafale jet fighters. The second case is under challenge in the Supreme Court. The analysis reveals how the Modi government has undermined democratic values and used constitutional provisions to pursue its partisan and ideological agenda.

The paper focuses attention on the often neglected topic of constitutional corruption in India.