Legitimate Information Research Articles

The health of cultural values and mahinga kai in the Te Wairoa-Hōpūpū-Hōnengenenge-Mātangirau, Wairoa river catchment and the impacts of sediment

ABSTRACT Te Wairoa Hōpūpū Hōnengenenge Mātangirau (Wairoa River) and its tributaries are significant to the iwi and hapū of Te Rohe o Te Wairoa in Wairoa, an area spanning northern Hawke’s Bay and southern Gisborne located on the Eastern side of Aotearoa-New Zealand’s (A-NZ) North Island. The Wairoa River is valued ecologically and for recreation and mahinga kai (food and resource gathering). The river is also affected by excess sediments. In this paper we present a cultural values framework developed in Wairoa and adapted to focus on the awa (river). Ninety two cultural value assessments were conducted with 55 people along reaches of the Wairoa river and the Waiau, its principal tributary. The data reveal considerable spatial and temporal variability in cultural health and add to knowledge of the direct and indirect impacts of sediment on cultural values. Furthermore, the data highlight the resiliency of cultural values of connectedness despite degradation to the environment, and that a range of methods can be employed to support or enhance the health of cultural values to complement water quality improvements. The use of this framework responds to the increasingly recognised need to include diverse perspectives in monitoring programmes and demonstrates that Māori-led cultural monitoring is a means to produce credible, relevant and legitimate information to support policy and planning.

Relative Performance Evaluation and Strategic Peer‐Harming Disclosures

ABSTRACTMany firms use relative stock performance to evaluate and incentivize their CEOs. We document that such firms routinely disclose information that harms their peers' stock prices, and sometimes explicitly mention the harmed peers, by name, in these disclosures. Consistent with deliberate sabotage, peer‐harming disclosures appear to be aimed at peers whose stock price depressions are most likely to benefit the disclosing firms' CEOs. The pricing effect of these disclosures does not reverse, suggesting that the disclosures contain legitimate information regarding peers' prospects. In sum, our results suggest that relative performance evaluation in CEO pay motivates CEOs to internalize the externalities of their disclosures, and strategically disclose information that harms peers' stock prices, in order to improve their firms' relative standing within their peer group.

Should Bank Stress Tests Be Fair?

Regulatory stress tests have become one of the main tools for setting capital requirements at the largest U.S. banks. The Federal Reserve uses confidential models to evaluate bank-specific outcomes for bank-specific portfolios in shared stress scenarios. As a matter of policy, the same models are used for all banks, despite considerable heterogeneity across institutions; individual banks have contended that some models are not suited to their businesses. Motivated by this debate, we ask, what is a fair aggregation of individually tailored models into a common model? We argue that simply pooling data across banks treats banks equally but is subject to two deficiencies: it may distort the impact of legitimate portfolio features, and it is vulnerable to implicit misdirection of legitimate information to infer bank identity. We compare various notions of regression fairness to address these deficiencies, considering both forecast accuracy and equal treatment. In the setting of linear models, we argue for estimating and then discarding centered bank fixed effects as preferable to simply ignoring differences across banks. We also discuss extensions to nonlinear models. This paper was accepted by Kay Giesecke, finance. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.02060 .

Malaysian Homeland Warriors’ Stories: Database repository design and implementation

Increasing demand for information retrieval on the World Wide Web and the need for a database repository to efficiently manage web content. Aim to design a repository focused on Malaysian Homeland Fighters, ensuring the secure preservation and retrieval of legitimate information about Malaysian historians and fighters. Combination of the Web Archive Life Cycle (WALC) and Database Life Cycle (DBLC) methodologies used to identify relevant web content, design the repository, and develop a user-friendly interface. This initiative aims to preserve and authenticate historical web content, serving as a knowledge hub for Malaysians to connect sustainably with the past.

Survey: Self-Empowered Wireless Sensor Networks Security Taxonomy, Challenges, and Future Research Directions

In the recent past, patient wearable devices and implantable biosensors revealed exponential growth in digital healthcare, because they have the capability to allow access to the information anywhere and every time to improve the life standard of multifarious disease effected patients followed by healthy people. Following these advantages, digital healthcare demands a secure wireless communication infrastructure for interconnected self-empowered biosensor devices to maintain the trust of patients, doctors, pharmacologists, nursing staff, and other associated stakeholders, etc. Several authentications, privacy, and data preservation schemes had been used in the literature to ensure the security of this emerging technology, but with time, these counteraction prototypes become vulnerable to new security threats, as the hackers work tirelessly to compromise them and steal the legitimate information of user’s or disrupt the operation of an employed self-empowered wireless sensor network (SWSN). To discuss the security problems of SWSN applications, in this review article, we have presented a detailed survey of the present literature from 2019 to 2022, to familiarize the readers with different security threats and their counteraction schemes. Following this, we will highlight the pros and cons of these countermeasure techniques in the context of SWSN security requirements to underscore their limitations. Thereafter, we will follow-up the underlined limitations to discuss the open security challenges of SWSN that need the concerned authorities’ attention. Based on this, we will pave a road map for the future research work that could be useful for every individual associated with this technology. For the novelty and uniqueness of this work, we will make comparative analysis with present survey papers published on this topic to answer the question of reviewers, readers, editors, and students that why this paper is in time and needed in the presence of rival papers.

IRS-Enabled Secure G2A Communications for UAV System With Aerial Eavesdropping

As one of the most promising new technologies in the upcoming 6G communication, intelligent reflecting surface (IRS) can significantly improve the reliability of wireless transmission and deal with the security threat in the presence of eavesdroppers by smartly reconfiguring the wireless propagation environment. However, existing research mainly focuses on terrestrial communication networks. In contrast, there is not much research on IRS-aided secure unmanned aerial vehicle (UAV) communication problems, especially for ground-to-air (G2A) communication scenarios with air eavesdroppers that will bring greater security threats. In this article, we consider an IRS-assisted G2A communication network with an altitude-dependent Rician fading channel, where the ground base station (BS) transmits legitimate information to the UAV user in the presence of air eavesdroppers. The transmission power, active and passive beamforming, and UAV 3-D trajectory are jointly optimized to maximize the average secrecy rate. Due to the nonconvexity of the optimization problem, the block coordinate descent method is used to solve it. Specifically, the overall optimization problem is divided into four subproblems, and nonconvex subproblems are solved by successive convex approximation and semidefinite relaxation algorithms. Simulation results show that the proposed alternating optimization algorithm has a good convergence performance and can increase the average secrecy rate by 28% compared with the scenario without IRS assistance.

Joint Information-Theoretic Secrecy and Covertness for UAV-Assisted Wireless Transmission With Finite Blocklength

In this paper, we study a novel joint information-theoretic secrecy and covertness model for an unmanned aerial vehicle (UAV)-assisted finite blocklength transmission system, in which an UAV delivers classified information to a ground user (Bob) in the presence of two unauthorized users (Eve and Willie). Specifically, Eve tries to decode the legitimate information while Willie tries to detect if the UAV's transmission is present or not. Firstly, we assume that the perfect location knowledge of both Eve and Willie is known at the UAV. In this scenario, to guarantee security and covertness, the average secrecy rate maximization problem is formulated via joint design of the UAV's transmit power and three dimensional (3D) trajectory under Willie's covertness constraint. Technically speaking, the optimization problem is non-convex and mathematically intractable to tackle owing to the coupling of multiple variables. To address this non-convex problem, an iterative alternating optimization (AO) algorithm is developed with the assistance of the successive convex approximation (SCA) technique. After that, we extend the system model to both Eve's and Willie's imperfect location knowledge case, which is more sophisticated compared to the perfect location knowledge case due to the uncertain location constraints. We still utilize the iterative AO algorithm combined with SCA and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {S}$</tex-math></inline-formula> -procedure techniques to cope with the nonconvex problem. Numerical results indicate that our proposed 3D optimization algorithm performs better than the conventional two dimensional (2D) scheme and the benchmark scheme for both perfect and imperfect location knowledge cases.

Bridging online and offline dynamics of the face mask infodemic

BackgroundOnline infodemics have represented a major obstacle to the offline success of public health interventions during the COVID-19 pandemic. Offline contexts have likewise fueled public susceptibility to online infodemics. We combine a large-scale dataset of Twitter conversations about face masks with high-performance machine learning tools to detect low-credibility information, bot activity, and stance toward face masks in online conversations. We match these digital analytics with offline data regarding mask-wearing and COVID-19 cases to investigate the bidirectional online-offline dynamics of the face mask infodemic in the United States.ResultsOnline prevalence of anti-mask over pro-mask stance predicts decreased offline mask-wearing behavior and subsequently increased COVID-19 infections. These effects are partially influenced by low-credibility information and automated bot activity, which consistently feature greater anti-mask stance online. Despite their purported controversy, mask mandates generally decrease anti-mask stance online and increase mask-wearing offline, thus reducing future COVID-19 infections. Notable asymmetries are observed, however, between states run by Democratic and Republican governors: the latter tend to see higher levels of low-credibility information and anti-mask stance online, and thus lower mask-wearing and higher infection rates offline.ConclusionsThese findings contribute new insights around collective vulnerabilities to online infodemics and their links to evolving offline crises. We highlight the need to synergize and sustain targeted online campaigns from legitimate information sources alongside offline interventions in and beyond the pandemic.

PENGARUH PENURUNAN TARIF PPH, SANKSI PAJAK DAN MODERNISASI PELAYANAN PAJAK TERHADAP KEPATUHAN WAJIB PAJAK UMKM JAKARTA BARAT SELAMA PANDEMI COVID-19

This research targets at how the impact of the very last PPH discount, tax sanctions, and tax service modernization on taxpayer compliance of west Jakarta MSMEs throughout the covid-19 pandemic. The purposive sampling method became used for this sample and the entire legitimate information turned into a hundred Respondents. The processing strategies the usage of a couple of regression evaluation information helped by using the SPSS software (Statistical Product and provider answer) for windows released 20 and Microsoft Excel 2013. The results of this have a look at implying that final PPH reduction, tax sanctions, and tax service modernization have effective effects on taxpayer compliance. The implication may be given as an enter and know-how approximately taxes and wish that by using the information this is given in the thing of tax compliance ought to help the authorities to elevate tax incomes.

Ethical Espionage: Balancing Privacy and Information Gathering

Abstract: Espionage, the covert collection of information, has long been a subject of controversy and debate. Recent technological advancements, particularly in mobile applications, have introduced discreet video recording, raising ethical concerns regarding privacy invasion and potential misuse. This research paper delves into the concept of ethical espionage by examining the implications, challenges, and potential benefits of an Android app designed for discreet video recording. It analyses the ethical considerations associated with such applications and proposes guidelines for responsible use, aiming to strike a balance between individual privacy and legitimate information gathering.

Phishing Susceptibility in Context: A Multilevel Information Processing Perspective on Deception Detection

Despite widespread awareness of risks, significant investments in cybersecurity protection, and substantial economic incentives to avoid security breaches, organizations remain vulnerable to phishing attacks. Phishing research has informed effective practical interventions to address phishing susceptibility that emphasize the importance of broadly applicable IT security knowledge. Yet employees still frequently fall victim to phishing attempts. To help understand why, we conceptualize phishing susceptibility as the failure to differentiate between deceptive and legitimate information processing requests that occur within the context of an employee’s typical job responsibilities. We apply this contextual lens to identify characteristics of knowledge workers’ organizational task and social context that may enhance or diminish performance in detecting deception in phishing email attempts. To test our hypotheses, we conducted a study in which employees of the finance division of a large university encountered simulated email-based phishing attempts as part of their normal work routine. We found evidence supporting our hypotheses that an individual’s susceptibility to phishing attacks is influenced by their position in the knowledge flows of the organization and by the impact of workgroup responsibilities on their cognitive processing. We contend that phishing susceptibility is not merely a matter of IT security knowledge but is also influenced by contextualized, multilevel influences on information processing. As phishing attacks are increasingly targeted to specific organizational settings, it is even more important to incorporate this contextualized information processing view of phishing susceptibility.

SecGreen: Secrecy Ensured Power Optimization Scheme for Software-Defined Connected IoV

Software-Defined Internet of Vehicles (SD-IoV) is an emerging technology that is being used in modern intelligent transportation systems (ITS). The ultimate goal of SD-IoV is to provide seamless connectivity to the end-users with low latency and high-speed data transfer. However, due to the increase in the density of the connected IoV using an open channel, i.e., the Internet, the foremost challenges of high power consumption and secure data transfer are inevitable in such an environment. An external eavesdropper may intercept the transmitted message to access the legitimate information over the public channel, i.e., the Internet. Most of the solutions reported in the literature to tackle these issues may not be applicable in the SD-IoV environment due to high computation and communication costs. Motivated from this, in this paper, the problems of high power consumption and secure data transfer in SD-IoV are formulated using mixed-integer non-linear programming (MINLP) with associated constraints. To solve the aforementioned problem, we propose a joint power optimization and secrecy ensured scheme known as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SecGreen</i> . <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SecGreen</i> has an efficient energy harvesting algorithm using simultaneous wireless information and power transfer (SWIPT) to maximize the energy efficiency. Moreover, to mitigate various security attacks, a resilient lightweight secrecy association protocol is designed between vehicle and trusted gateway node of SD-IoV so that only trusted vehicles can communicate with each other and with the nearest base stations. The secrecy association protocol uses security primitives such as– physically unclonable function (PUF), one-way hash function, and bitwise exclusive OR (XOR) operations which are suitable for energy-constraint sensors in SD-IoV. The performance of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SecGreen</i> is compared with the existing schemes, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Stable &amp; Scalable Link Optimization (SSLO), and Secure &amp; Energy-Efficient Blockchain-enabled (SEEB)</i> respectively. The result shows that when the number of packets across the subchannel increases, the energy consumption increases. Also, the result shows that the proposed scheme attains 22.5% and 20.34% better energy efficiency as compared to SSLO and SEEB schemes, respectively. In addition, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SecGreen</i> scheme achieves 37.48% and 32.15% higher throughput as compared to SSLO and SEEB schemes. The results obtained show the superior performance of the proposed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SecGreen</i> scheme in comparison to these existing competitive schemes in the literature.

QUALITATIVE EVIDENCE ON CHILLING EFFECTS—HOW USERS’ IMAGINARIES OF DATAVEILLANCE LEAD TO INHIBITED DIGITAL BEHAVIOR

Our everyday life has become highly digitized, and all our online actions leave digital data traces that are automatically collected, aggregated, and analyzed. Emerging research shows that this dataveillance can lead to chilling effects on mundane and legitimate information and communication behaviors. However, the extent to which a sense of dataveillance deters individuals from freely engaging in legitimate digital behaviors remains empirically understudied. Therefore, we ask: How do internet users imagine dataveillance? And how does this perception lead to people limiting their digital behavior? To address these questions, we conduct qualitative semi-structured interviews with 15 individuals. Our results contribute to a better understanding of people’s imaginaries of dataveillance, as well as sources for these perceptions and reactions towards them in terms of chilled digital behaviors. We show how imagined sources of dataveillance can range from states to corporate actors and how knowledge about dataveillance can originate in news coverage on data scandals or own experiences. Also, we identify further legitimate, mundane behaviors in other life domains like participating in online dating and buying things online that are affected by a sense of dataveillance and have so far been neglected in research on chilling effects. These results contribute to an empirically founded understanding of the nature of a sense of dataveillance and resulting chilling effects on digital behavior. They provide the basis for a representative survey and a mobile experience sampling study as part of a mixed-methods research design investigating chilling effects on a population level.

A Query-Based Greedy Approach for Authentic Influencer Discovery in SIoT

The authors propose an informed search greedy approach that efficiently identifies the influencer nodes in the social Internet of Things with the ability to provide legitimate information. Primarily, the proposed approach minimizes the network size and eliminates undesirable connections. For that, the proposed approach ranks each of the nodes and prioritizes them to identify an authentic influencer. Therefore, the proposed approach discards the nodes having a rank (α) lesser than 0.5 to reduce the network complexity. α is the variable value represents the rank of each node that varies between 0 to 1. Node with the higher value of α gets the higher priority and vice versa. The threshold value α = 0.5 defined by the authors with respect to their network pruning requirements that can be vary with respect to other research problems. Finally, the algorithm in the proposed approach traverses the trimmed network to identify the authentic node to obtain the desired information. The performance of the proposed method is evaluated in terms of time complexity and accuracy by executing the algorithm on both the original and pruned networks. Experimental results show that the approach identifies authentic influencers on a resultant network in significantly less time than in the original network. Moreover, the accuracy of the proposed approach in identifying the influencer node is significantly higher than that of the original network. Furthermore, the comparison of the proposed approach with the existing approaches demonstrates its efficiency in terms of time consumption and network traversal through the minimum number of hops.

Secrecy Outage Analysis of Relay-User Pairing for Secure Hybrid Satellite-Terrestrial Networks

In this paper, we study the physical-layer security of a hybrid satellite-terrestrial network composed of a satellite, multiple terrestrial decode-and-forward (DF) relays and users in the presence of a terrestrial eavesdropper who attempts to tap legitimate information from both satellite and relays. We consider two different scenarios: I) the channel state information (CSI) of wiretap links is known, and II) the CSI of wiretap links is unknown. Accordingly, optimal and suboptimal relay-user pairing schemes are proposed for the aforementioned two scenarios, respectively. Specifically, a relay-user pair maximizing the secrecy rate is chosen as the optimal one to participate in the transmission, where all links’ CSI is used to perform relay-user pairing. However, suboptimal relay-user pairing scheme is designed only relying on the main links’ CSI without requiring wiretap links’ CSI, where a relay-user pair with the maximal transmission rate of main link is selected. Also, closed-form expressions of secrecy outage probability with both integer and rational fading severity parameter are derived in the case of satellite links undergo Shadowed-Rician fading and terrestrial links subject to Rayleigh fading. Furthermore, asymptotic secrecy outage probability analysis is conducted in the high signal-to-noise ratio region. Numerical results reveal that relay-user pairing is beneficial to further improve physical-layer security of the hybrid satellite-terrestrial network.

Robust Design for Intelligent Reflecting Surface-Assisted Secrecy SWIPT Network

This paper investigates the robust beamforming design in a secrecy multiple-input single-output (MISO) network aided by the intelligent reflecting surface (IRS) with simultaneous wireless information and power transfer (SWIPT). Specifically, by considering that the energy receivers (ERs) are potential eavesdroppers (Eves) and imperfect channel state information (CSI) of the direct and cascaded channels can be obtained, we investigate the max-min fairness robust secrecy design. The objective is to maximize the minimum robust information rate among the legitimate information receivers (IRs). To solve the formulated non-convex design problem in bounded and probabilistic CSI error models, we utilize the alternating optimization (AO) and successive convex approximation (SCA) methods to obtain an approximate problem. Then, an iteration-based algorithm framework was proposed, where the unit modulus constraint (UMC) of the IRS is handled by the penalty dual decomposition (PDD) method. Moreover, a stochastic SCA method is proposed to handle the outage constrained design with statistical CSI. Finally, simulation results validate the promising performance of the proposed design.

From Statistics to Stories: Indices and Indicators as Communication Tools for Social Change

The terms ‘indices’ and ‘indicators’ may immediately cause eyelids to droop. How, then, might they serve to impassion publics and, ultimately, promote social change? This paper examines the extent to which indices and indicators can be considered communication tools for social movements and social change. The analysis is based on a 2018 evaluation of one index based in the United States – the Ranking Digital Rights Index, which assesses privacy and freedom of expression in the ICT space – and incorporates interviews with civil society stakeholders. Bringing theory from the fields of journalism and social movements together with the data from the evaluation, the findings suggest indices can serve as useful communication resources for social movements under certain circumstances. In particular, the analysis suggests three communication resources – legitimate information, newsworthy information, and flexible information – that human rights indices are most likely to provide.

Intelligent Jamming Strategies for Secure Spectrum Sharing Systems

This paper investigates the secrecy performance of a spectrum sharing network, where <inline-formula> <tex-math notation="LaTeX">${N}$ </tex-math></inline-formula> legitimate source-destination pairs orderly access the shared spectrum for communication, while an eavesdropper (E) attempts to tap the legitimate information transmission. To improve the physical layer security, we propose two jamming strategies that can intelligently switch between jamming and non-jamming, namely, suboptimal jammer selection (SJS) scheme and optimal jammer selection (OJS) scheme. Specifically, when a user pair is assigned to access the shared spectrum, another source is chosen as a friendly jammer in order to create intentional interference at E. For the purpose of comparison, we present the non-jammer selection (NJS) scheme as a benchmark. Analytical closed-form secrecy outage probability expressions of NJS, SJS and OJS schemes are derived over Nakagami-<inline-formula> <tex-math notation="LaTeX">${m}$ </tex-math></inline-formula> fading channels. We further present an asymptotic secrecy outage probability analysis to evaluate the secrecy diversity gain performance of NJS, SJS and OJS schemes. Numerical results show that the secrecy outage probability performance of OJS scheme is better than SJS and NJS schemes in the low average channel power gain <inline-formula> <tex-math notation="LaTeX">${\mathop \Omega \nolimits _{D} }$ </tex-math></inline-formula> region. Furthermore, the secrecy outage probabilities of NJS as well as SJS and OJS schemes converge to each other with the increase of <inline-formula> <tex-math notation="LaTeX">${\mathop \Omega \nolimits _{D} }$ </tex-math></inline-formula>, due to the fact that the OJS and SJS scheme will switch to NJS scheme when <inline-formula> <tex-math notation="LaTeX">${\mathop \Omega \nolimits _{D} }$ </tex-math></inline-formula> tends to infinity.

Questioning the legitimacy of bariatric surgery: a qualitative analysis of individuals from the community who qualify for bariatric surgery.

BackgroundLittle is known about how individuals in the community who qualify for bariatric surgery perceive it and how this affects their likelihood to consider it for themselves. This study is the first qualitative study of a racially and ethnically diverse cohort to understand perceptions of bariatric surgery.MethodsWe designed a descriptive study to understand attitudes about bariatric surgery. We interviewed 32 individuals who met NIH criteria for bariatric surgery but have never considered bariatric surgery. We purposively sampled to ensure the majority of participants were non-white. Using an Interpretive Description framework, an exploratory, iterative method was used to code interviews and arrive at final themes.ResultsParticipants self-identified as 88% female, 75% Black, 3% Hispanic, 3% Pacific Islander, and 19% white. Three major themes emerged from our data regarding legitimacy of bariatric surgery. First, participants perceived bariatric surgery to be something commercialized rather than needed treatment. They equated bariatric surgery with “botulism of the lips” or “cool sculpting.” Second, an important contributor to the lack of legitimacy as a medical treatment was that many had not heard about bariatric surgery before from their doctors. Doctors were trusted sources for legitimate information about health. Lastly, conflicting information over bariatric surgery-related diet and weight loss further diminished the legitimacy of bariatric surgery. As one participant reflected about pre-operative weight loss requirements, “[If] I'm going to do that, I might as well just keep losing the weight. Why even go do the surgery?”.ConclusionThough bariatric surgery is a safe, effective, and durable therapy for patients with obesity, the majority of individuals we interviewed had concerns over the legitimacy of bariatric surgery as a medical treatment. Moving forward in reaching out to communities about bariatric surgery, healthcare providers and systems should consider the presentation of information to attenuate these concerns.

A Novel Evolving Sentimental Bag-of-Words Approach for Feature Extraction to Detect Misinformation

The state-of-the-art misinformation detection techniques mainly focus on static datasets. However, a massive amount of information is generated online and the websites are flooded with this legitimate information and misinformation. It is difficult to keep track of this changing information and provide up-to-date accurate status of webpages giving either legitimate information or misinformation. Therefore, to keep the features up-to-date, authors have proposed evolving sentimental Bag-of-Words approach. This involves, updating sentimental features every time the new or changed web contents are read. This process accumulates the sentimental features at different time intervals that can be utilized to detect misinformation in URLs and upgrade the status of the webpage with timely information. Apart from sentimental features, other state-of-the-art features viz. syntactical, Part-Of-Speech Tagging (POST), and Term-Frequency (TF) are updated in a timely manner and utilized to detect misinformation. The model performed well with the support vector machine showing an accuracy of 80% while the decision tree classifier showed less accuracy of 56.66%.