Modeling and Predicting of News Popularity in Social Media Sources

Akyol, Kemal; Şen, Baha

doi:10.32604/cmc.2019.08143

Cited by 9 publications

(8 citation statements)

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“…A brief overview of notable relevant works to the proposed model is given in this section. According to the literature, a traditional Machine Learning (ML) [ 1 , 2 , 9 , 22 , 30 , 31 , 33 ] and Deep Learning (DL) [ 5 , 7 , 18 , 25 , 41 , 47 , 54 , 55 ] techniques effectively used textual, visual, and social-context features to solve the automated fake news detection problem. Furthermore, existing research is divided into two categories: unimodal and multimodal fake news identification.…”

Section: Related Workmentioning

confidence: 99%

“…Furthermore, existing research is divided into two categories: unimodal and multimodal fake news identification. To detect fake news, the former uses either one of the content-based [ 1 , 2 , 5 , 7 , 9 , 18 , 22 , 25 , 30 , 31 , 33 , 41 , 47 , 54 , 55 ] or social-context-based features [ 13 , 23 , 24 , 26 , 27 , 38 , 50 , 51 ], while the latter uses a combination of any single modality feature [ 20 , 43 , 44 , 49 , 53 ].…”

Section: Related Workmentioning

confidence: 99%

“…However, these methods are time-consuming and labor-intensive, since it needs hand-crafted features. Akyol et al [ 2 ] designed gradient boost tree (GBT), multilayer perceptron and random forest (RF) to identify fake news).…”

Section: Related Workmentioning

confidence: 99%

“…Unimodal content-based FND approaches are ineffective at detecting fake news because they use textual [ 1 , 2 , 5 , 7 , 9 , 18 , 22 , 25 , 30 , 31 , 33 , 41 , 47 , 54 , 55 ] and visual features [ 4 , 11 , 14 , 28 , 34 , 56 , 59 ] separately. In general, no underlying semantics were discovered among these features, resulting in poor classification.…”

Section: Introductionmentioning

confidence: 99%

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CB-Fake: A multimodal deep learning framework for automatic fake news detection using capsule neural network and BERT

Palani

Elango

K³

2021

Multimed Tools Appl

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The progressive growth of today’s digital world has made news spread exponentially faster on social media platforms like Twitter, Facebook, and Weibo. Unverified news is often disseminated in the form of multimedia content like text, picture, audio, or video. The dissemination of such false news deceives the public and leads to protests and creates troubles for the public and the government. Hence, it is essential to verify the authenticity of the news at an early stage before sharing it with the public. Earlier fake news detection (FND) approaches combined textual and visual features, but the semantic correlations between words were not addressed and many informative visual features were lost. To address this issue, an automated fake news detection system is proposed, which fuses textual and visual features to create a multimodal feature vector with high information content. The proposed work incorporates the bidirectional encoder representations from transformers (BERT) model to extract the textual features, which preserves the semantic relationships between words. Unlike the convolutional neural network (CNN), the proposed capsule neural network (CapsNet) model captures the most informative visual features from an image. These features are combined to obtain a richer data representation that helps to determine whether the news is fake or real. We investigated the performance of our model against different baselines using two publicly accessible datasets, Politifact and Gossipcop. Our proposed model achieves significantly better classification accuracy of 93% and 92% for the Politifact and Gossipcop datasets, respectively, compared to 84.6% and 85.6% for the SpotFake+ model.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

CB-Fake: A multimodal deep learning framework for automatic fake news detection using capsule neural network and BERT

Palani

Elango

K³

2021

Multimed Tools Appl

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“…Users, online friendships, and generated tweets jointly form a virtual world. As a linkage between the virtual world and the real society, the location of microblog users could be utilized for many location-based applications, such as targeted advertising, regional communities discovering, news popularity predicting, and public opinion monitoring [1][2][3][4]. Due to the restriction of privacy protection [5], the locations of microblog users can only be obtained from the public profiles and geo-tags.…”

Section: Introductionmentioning

confidence: 99%

Microblog User Geolocation by Extracting Local Words Based on Word Clustering and Wrapper Feature Selection

2020

KSII TIIS

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Existing methods always rely on statistical features to extract local words for microblog user geolocation. There are many non-local words in extracted words, which makes geolocation accuracy lower. Considering the statistical and semantic features of local words, this paper proposes a microblog user geolocation method by extracting local words based on word clustering and wrapper feature selection. First, ordinary words without positional indications are initially filtered based on statistical features. Second, a word clustering algorithm based on word vectors is proposed. The remaining semantically similar words are clustered together based on the distance of word vectors with semantic meanings. Next, a wrapper feature selection algorithm based on sequential backward subset search is proposed. The cluster subset with the best geolocation effect is selected. Words in selected cluster subset are extracted as local words. Finally, the Naive Bayes classifier is trained based on local words to geolocate the microblog user. The proposed method is validated based on two different types of microblog data-Twitter and Weibo. The results show that the proposed method outperforms existing two typical methods based on statistical features in terms of accuracy, precision, recall, and F1-score.

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Analysis of Stock Market Public Opinion Based on Web Crawler and Deep Learning Technologies Including 1DCNN and LSTM

Chen

Zhou

et al. 2022

Arab J Sci Eng

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Modeling and Predicting of News Popularity in Social Media Sources

Cited by 9 publications

References 0 publications

CB-Fake: A multimodal deep learning framework for automatic fake news detection using capsule neural network and BERT

CB-Fake: A multimodal deep learning framework for automatic fake news detection using capsule neural network and BERT

Microblog User Geolocation by Extracting Local Words Based on Word Clustering and Wrapper Feature Selection

Analysis of Stock Market Public Opinion Based on Web Crawler and Deep Learning Technologies Including 1DCNN and LSTM

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