Adaptive Salp swarm optimization algorithms with inertia weights for novel fake news detection model in online social media

Özbay, Feyza Altunbey; Alataş, Bilal

doi:10.1007/s11042-021-11006-8

Cited by 35 publications

(13 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…As we can see in fig. (7), our proposed model is underperforming the best model and has 94% accuracy, almost .7% lesser accuracy than the highest accuracy claim by the best algorithm, which is 95% in this dataset [23]. Our model is more consistent in every epoch we run while testing it on this dataset, but it cannot best the accuracy in this dataset compared to the existing model, which we can improve in feature.…”

Section: ) Isotmentioning

confidence: 72%

“…On the FakeNewsNet dataset BERT approach is used in [35] is giving maximum accuracy among all the other models. On the BUZZFEED dataset ASSO-OSSIW approach is used in [23] is giving maximum accuracy among all the other models; on Weibo, the dataset CNN approach is used in [42] is giving consistent accuracy in every epoch among all the other models, on LIAR dataset NLP approach is used in [43] is giving maximum accuracy among all the other models, on PHEME dataset MN approach is used in [42] is giving maximum accuracy among all the other models.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Fake News Detection System Using Featured-Based Optimized MSVM Classification

et al. 2022

View full text Add to dashboard Cite

Fake News creates erroneous suspense information that can be identified. This spreads dishonesty about a country's status or overstates the expense of special functions for a government, destroying democracy in certain countries, such as in the Arab Spring. Associations such as the ''House of Commons and the Crosscheck project'' address concerns such as publisher responsibility. However, since they rely entirely on manual detection by humans, their coverage is minimal. This is neither sustainable nor possible in a world where billions of items are withdrawn or posted every second. The paper produces a deep study on past research work on fake news detection on the selected data-sets and proposes an algorithm with Multi-layered Principal Component Analysis for feature selection followed by firefly-optimized algorithm. Multi-Support Vector Machines(MSVM) are finally used to classify the news. We used ten different data-sets for testing the proposed algorithm. As the number of features in the data-sets are more, feature extration and selection methods help to improve the accuracy in respective data-sets. Only the datasets having less number of features gave a lower performance on our feature extraction algorithms. INDEX TERMSFake news, rirefly, long short-term memory (LSTM), multi support vector machine (MSVM).

show abstract

Section: ) Isotmentioning

confidence: 72%

Section: Related Workmentioning

confidence: 99%

Fake News Detection System Using Featured-Based Optimized MSVM Classification

et al. 2022

View full text Add to dashboard Cite

show abstract

“…To investigate the performance of OBDSSA, this subsection compares it with several SSA variants on a set of 18 widely used benchmarks listed in Table 4. The compared algorithms include the original SSA [37], the self‐adaptive SSA algorithm (ASSA) [51], the SSA algorithm with random replacement tactic and double adaptive weighting mechanism (RDSSA) [53], the chaotic mechanism‐based SSA algorithm (CSSA) [59], the enhanced SSA algorithm (ESSA) [60], the PSO‐based SSA algorithm (SSAPSO) [61], the lifetime mechanism‐based SSA algorithm (LSSA) [62], the multi‐subpopulation‐based SSA algorithm (MSNSSA) [63], the opposition‐based learning enhanced SSA algorithm (ISSA) [64], the Gaussian‐SSA algorithm (GSSA) [65], the enhanced opposition‐based SSA algorithm (OBSSA) [66], the adaptive SSA algorithm with non‐linear coefficient decreasing inertia weight (ASSO) [67], the hybrid enhanced whale optimisation SSA algorithm (IWOSSA) [68]. In this experiment, mean value (Mean) and standard deviation (Std) were employed to evaluate the performance of the algorithms, the dimension of the benchmark functions was set as 100.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…OGWO a was nonlinearly decreased from 2 to 0 SSA algorithm (MSNSSA) [63], the opposition-based learning enhanced SSA algorithm (ISSA) [64], the Gaussian-SSA algorithm (GSSA) [65], the enhanced opposition-based SSA algorithm (OBSSA) [66], the adaptive SSA algorithm with nonlinear coefficient decreasing inertia weight (ASSO) [67], the hybrid enhanced whale optimisation SSA algorithm (IWOSSA) [68]. In this experiment, mean value (Mean) and standard deviation (Std) were employed to evaluate the performance of the algorithms, the dimension of the benchmark functions was set as 100.…”

Section: Comparison With Ssa and Improved Ssamentioning

confidence: 99%

Advanced orthogonal opposition‐based learning‐driven dynamic salp swarm algorithm: Framework and case studies

Wang

Ding

Yang

et al. 2022

IET Control Theory & Appl

View full text Add to dashboard Cite

Salp swarm algorithm (SSA) is a relatively new bio‐inspired meta‐heuristic optimization algorithm that mimics the navigating and foraging behavior of salps in oceans. This paper presents an orthogonal quasi‐opposition‐based learning‐driven dynamic SSA (OBDSSA) for solving global optimization problems. The proposed methodology integrates orthogonal quasi‐opposition‐based learning (OQOBL) tactic and dynamic learning (DL) strategy with SSA to improve its performance. The OQOBL technique is used to enrich the exploration and development capability of the canonical SSA to help the salp chain jump out of local optimum, while the DL mechanism is applied to the basic approach to expand the neighborhood searching capabilities of the search agents. To investigate the proposed operators and OBDSSA algorithm, 18 widely used benchmark functions and parameter extraction problem of photovoltaic (PV) model have been experimented upon. The comparisons reveal that OBDSSA outperforms all competitors, including the standard SSA, SSA variants, and other state‐of‐the‐art algorithms. Finally, the developed approach is applied to path planning and obstacle avoidance (PPOA) tasks in autonomous mobile robots (AMR) and satisfactory results are obtained.

show abstract

“…While compared with the shallow models, deep learning methods have considerable features for large datasets in terms of interpretability, learning capacity, feature representation, number of parameters, and running time. Similarly, some miscellaneous techniques are used for detecting fake news, which is the reverse-tracking approach (Ko et al June 2019), honeycomb framework (Talwar et al 2020), and ASSO-OSIW and GWO (Ozbay and Alatas 2021). The most used techniques with some advantages and challenges are listed in Table 3.…”

Section: Algorithmic Classificationmentioning

confidence: 99%

A systematic literature review and existing challenges toward fake news detection models

Shah

Ganatra

2022

Soc. Netw. Anal. Min.

View full text Add to dashboard Cite

Emerging of social media creates inconsistencies in online news, which causes confusion and uncertainty for consumers while making decisions regarding purchases. On the other hand, in existing studies, there is a lack of empirical and systematic examination observed in terms of inconsistency regarding reviews. The spreading of fake news and disinformation on social media platforms has adverse effects on stability and social harmony. Fake news is often emerging and spreading on social media day by day. It results in influencing or annoying and also misleading nations or societies. Several studies aim to recognize fake news from real news on online social media platforms. Accurate and timely detection of fake news prevents the propagation of fake news. This paper aims to conduct a review on fake news detection models that is contributed by a variety of machine learning and deep learning algorithms. The fundamental and well-performing approaches that existed in the past years are reviewed and categorized and described in different datasets. Further, the dataset utilized, simulation platforms, and recorded performance metrics are evaluated as an extended review model. Finally, the survey expedites the research findings and challenges that could have significant implications for the upcoming researchers and professionals to improve the trust worthiness of automated fake news detection models.

show abstract

Adaptive Salp swarm optimization algorithms with inertia weights for novel fake news detection model in online social media

Cited by 35 publications

References 28 publications

Fake News Detection System Using Featured-Based Optimized MSVM Classification

Fake News Detection System Using Featured-Based Optimized MSVM Classification

Advanced orthogonal opposition‐based learning‐driven dynamic salp swarm algorithm: Framework and case studies

A systematic literature review and existing challenges toward fake news detection models

Contact Info

Product

Resources

About