Persuading users into verifying online fake news

Abstract. Checking authenticity of fake news before sharing online can reduce spread of misinformation. But fact-checking requires cognitive and psychological effort, which people are often not willing to give. Some fact-checking methods might even be counterproductive, entrenching people into their deeply held beliefs. Numerous online fact-checking services have emerged recently which verify false claims to address the issue. While these services are quite efficient technologically, they seriously overlook human behavioral factors associated with fake news. Persuasive systems have been proven successful in attitudinal and behavioral changes, which could be applied here as behavioral interventions for fact-checking. A review of current fact-checking services showed that they significantly lack persuasive features, resulting in a passive and linear user experience. Findings from cognitive science and persuasion literature paved way for development of a fact-checking mobile application that would encourage users into regular fact-checking. Qualitative and quantitative evaluation of the artifact showed promise of persuasion in combating fake news. Social support persuasive features were found most effective, followed by tunnelling and self-monitoring. Implications of these findings and future research directions are discussed

Assessing the impacts of climate and land use and land cover change on the freshwater availability in the Brahmaputra River basin

AbstractStudy Region: Brahmaputra River basin in South Asia.Study Focus: The Soil and Water Assessment Tool was used to evaluate sensitivities and patterns in freshwater availability due to projected climate and land use changes in the Brahmaputra basin. The daily observed discharge at Bahadurabad station in Bangladesh was used to calibrate and validate the model and analyze uncertainties with a sequential uncertainty fitting algorithm. The sensitivities and impacts of projected climate and land use changes on basin hydrological components were simulated for the A1B and A2 scenarios and analyzed relative to a baseline scenario of 1988–2004.New hydrological insights for the region: Basin average annual ET was found to be sensitive to changes in CO2 concentration and temperature, while total water yield, streamflow, and groundwater recharge were sensitive to changes in precipitation. The basin hydrological components were predicted to increase with seasonal variability in response to climate and land use change scenarios. Strong increasing trends were predicted for total water yield, streamflow, and groundwater recharge, indicating exacerbation of flooding potential during August–October, but strong decreasing trends were predicted, indicating exacerbation of drought potential during May–July of the 21st century. The model has potential to facilitate strategic decision making through scenario generation integrating climate change adaptation and hazard mitigation policies to ensure optimized allocation of water resources under a variable and changing climate

Differential Heating in the Indian Ocean Differentially Modulates Precipitation in the Ganges and Brahmaputra Basins

Indo-Pacific sea surface temperature dynamics play a prominent role in Asian summer monsoon variability. Two interactive climate modes of the Indo-Pacific—the El Niño/Southern Oscillation (ENSO) and the Indian Ocean dipole mode—modulate the amount of precipitation over India, in addition to precipitation over Africa, Indonesia, and Australia. However, this modulation is not spatially uniform. The precipitation in southern India is strongly forced by the Indian Ocean dipole mode and ENSO. In contrast, across northern India, encompassing the Ganges and Brahmaputra basins, the climate mode influence on precipitation is much less. Understanding the forcing of precipitation in these river basins is vital for food security and ecosystem services for over half a billion people. Using 28 years of remote sensing observations, we demonstrate that (i) the tropical west-east differential heating in the Indian Ocean influences the Ganges precipitation and (ii) the north-south differential heating in the Indian Ocean influences the Brahmaputra precipitation. The El Niño phase induces warming in the warm pool of the Indian Ocean and exerts more influence on Ganges precipitation than Brahmaputra precipitation. The analyses indicate that both the magnitude and position of the sea surface temperature anomalies in the Indian Ocean are important drivers for precipitation dynamics that can be effectively summarized using two new indices, one tuned for each basin. These new indices have the potential to aid forecasting of drought and flooding, to contextualize land cover and land use change, and to assess the regional impacts of climate change

Bengali Fake Review Detection using Semi-supervised Generative Adversarial Networks

This paper investigates the potential of semi-supervised Generative Adversarial Networks (GANs) to fine-tune pretrained language models in order to classify Bengali fake reviews from real reviews with a few annotated data. With the rise of social media and e-commerce, the ability to detect fake or deceptive reviews is becoming increasingly important in order to protect consumers from being misled by false information. Any machine learning model will have trouble identifying a fake review, especially for a low resource language like Bengali. We have demonstrated that the proposed semi-supervised GAN-LM architecture (generative adversarial network on top of a pretrained language model) is a viable solution in classifying Bengali fake reviews as the experimental results suggest that even with only 1024 annotated samples, BanglaBERT with semi-supervised GAN (SSGAN) achieved an accuracy of 83.59% and a f1-score of 84.89% outperforming other pretrained language models - BanglaBERT generator, Bangla BERT Base and Bangla-Electra by almost 3%, 4% and 10% respectively in terms of accuracy. The experiments were conducted on a manually labeled food review dataset consisting of total 6014 real and fake reviews collected from various social media groups. Researchers that are experiencing difficulty recognizing not just fake reviews but other classification issues owing to a lack of labeled data may find a solution in our proposed methodology

A Fuzzy ANP Based Grey Relational Approach to Evaluate CRM System in Context of Bangladesh

This study aims to select a suitable CRM (customer relationship management) system among different possible alternatives for organization’s in Bangladesh. Since, evaluating CRM system on the basis of lot of attributes leads us to Multiple-criteria decision analysis (MCDA) problems. In this study, a hybrid MCDA models were used. FuzzyANP (Analytic Network Process) and GRA (Grey Relational Analysis) approaches were adopted to solve the problem. The study explored that the Hubspot CRM was optimal solution in context of Bangladesh. Our research will beneficial to the organizing for better customer support. As far our knowledge goes, this is the first attempt to select CRM softwares in context of Bangladesh. Keywords: Analytic network process; Customer relationship management system; Grey relational analysis; Multiple-criteria decision analysis DOI: 10.7176/IKM/11-4-06 Publication date:June 30th 202