Liar dataset

Author: t | 2025-04-25

A copy of the LIAR dataset from the Wang paper - Liar, Liar Pants on Fire : A New Benchmark Dataset for Fake News Detection. - dgauraang/liar-dataset

ssunwalka01/LIAR-LIAR-Dataset-FAKE_NEWS_Classification

17.000000, 75% 22.000000, max 66.000000. These statistics show that there are articles with only one word in the dataset, so it has been decided to remove all rows with less than 10 words as they are considered poorly informative. The resulting dataset contains 1657 less rows than the original one. The updated statistics are reported in what follows: count 13732.000000, mean 19.228663, std 8.192268, min 10.000000, 25% 14.000000, 50% 18.000000, 75% 23.000000, max 66.000000. Finally, the average number of words per article is 19. FakeNewsNet This dataset has been built by gathering information from two fact-checking websites to obtain news contents for fake news and real news such as PolitiFact and GossipCop. In PolitiFact, journalists and domain experts review the political news and provide fact-checking evaluation results to claim news articles as fake or real. Instead, in GossipCop, entertainment stories, from various media outlets, are evaluated by a rating score on the scale of 0 to 10 as the degree from fake to real. The dataset contains about 900 political news and 20k gossip news and has only two labels: true and false [14]. This dataset is publicly available using the functions provided by the FakeNewsNet team and the Twitter API. As mentioned above, FakeNewsNet can be split in two subsets: GossipCop and Politifact.com. We decided to analyse only political news as they produce worse consequences in real world than gossip ones. The dataset is well balanced and contains 434 real news and 367 fake news. Most of the news regards the US as it has been evaluated also in LIAR. Fake news topics concern Obama, police, Clinton and Trump while real news topics refer to Trump, Republicans and Obama. Such as the LIAR dataset, it has been added a new column and used the command df.describe() to print out the following statistical information: count 801, mean 1459.217228, std 3141.157565, min 3, 25% 114, 50% 351, 75% 893, max 17377. The average number of words per articles in Politifact dataset is 1459, which is far longer than the average sentence length in Liar Dataset that is 19 words per articles.

GitHub - dgauraang/liar-dataset: A copy of the LIAR dataset from

Of samples used, before updating the internal model parameters. Possible values are 16, 32, 64. We found in our experiments that the optimal value is 32. 4 Experimental setupThis section aims to describe the experimental setup for the adopted benchmark system.4.1 DatasetWe analyzed different Fake News datasets, publicly available, that differ in quantity, type of news and sentence length. In particular, we focused our attention on: Liar, FakeNewsNet and PHEME Datasets (Wang, 2017; Shu et al., 2018) that are described in details in what follows. Liar dataset This dataset includes 12.8K human labelled short statements from fact-checking website Politifact.com. Each statement is evaluated by a Politifact.com editor for its truthfulness. The dataset has six fine-grained labels: pants-fire, false, barely-true, half-true, mostly-true, and true. The distribution of labels is relatively well- balanced. For our purposes the six fine-grained labels of the dataset have been collapsed in a binary classification, i.e., label 1 for fake news and label 0 for reliable ones. This choice has been made for comparison purposes due to binary Fake News Dataset feature. The dataset is partitioned into three files: 1) Training Set: 5770 real news and 4497 fake news; 2) Test Set: 1382 real news and 1169 fake news; 3) Validation Set: 1382 real news and 1169 fake news. The three subsets are well balanced so there is no need to perform oversampling or undersampling. The corresponding Wordclouds for fake news is reported in Fig. 5a. It is easy to see that news are mainly related to United States. Fake news topics are collected about Obama, Obamacare, Cicilline, Romney.Fig. 5LIAR Fake (a) and Real (b) WordcloudsFull size image On the other side real news topics depicted in Fig. 5b refer to McCain, elections and Obama. The processed dataset has been uploaded in Google Drive and, then, loaded in Colab’s Jupyter as a Pandas Dataframe. It has been added a new column with the number of words for each article row. Using the command df.describe() on this column it is possible to print the following statistical information: count 15389.000000, mean 17.962311, std 8.569879, min 1.000000, 25% 12.000000, 50%

Structure of the LIAR dataset

We checked for updates on 131 streaming services on March 18, 2025 at 2:23:02 a.m..Something wrong? Let us know! Liar, Liar, Vampire - watch online: streaming, buy or rent Currently you are able to watch "Liar, Liar, Vampire" streaming on Paramount Plus, Paramount+ Amazon Channel, Paramount Plus Apple TV Channel , Paramount Plus Premium, Paramount Plus Basic with Ads or for free with ads on Pluto TV. It is also possible to rent "Liar, Liar, Vampire" on Apple TV online and to download it on Apple TV. Where can I watch Liar, Liar, Vampire for free? Liar, Liar, Vampire is available to watch for free today. If you are in Canada, you can: Stream it online with ads on Pluto TV If you’re interested in streaming other free movies and TV shows online today, you can: Watch movies and TV shows with a free trial on Prime Video Synopsis When ordinary boy Davis suddenly becomes famous at school as people start to believe he's actually a vampire, vampire expert Cameron helps him act like a real vampire. Cast People who liked Liar, Liar, Vampire also liked Popular movies coming soon Upcoming Comedy movies Similar Movies you can watch for free More popular Movies directed by Vince Marcello. A copy of the LIAR dataset from the Wang paper - Liar, Liar Pants on Fire : A New Benchmark Dataset for Fake News Detection. - dgauraang/liar-dataset

LIAR Dataset - Machine Learning Datasets

Matrix for LIAR (a) and Polifact (b) datasetsFull size image5.2 Deep learning evaluationAs mentioned above, the deep neural networks evaluation has been initially performed by training the models using the basic parameters in Table 6. The performances evaluated on Liar and PolitiFact datasets are shown in Tables 10 and 11.Table 10 Results on LIAR for deep learning with basic parametersFull size tableTable 11 Results on PoliFact for deep learning with basic parametersFull size tableAs easy to note, Google B.E.R.T. obtained the overall best results both on Liar Test and PolitiFact Tests. Nevertheless, as mentioned in previous section, we performed a parameter optimization of B.E.R.T. network (reported in Table 7) for analyzing the (eventual) performance improvement. More in details, Table 12 shows that the performances increase in terms of accuracy, precision and AUCTable 12 Results of Deep Learning B.E.R.T. on Polifact and LIAR datasetsFull size tableWe hypothesize that our results are quite better due to the fine hyper parameter tuning we performed, a better pre-processing step and the proper transformation we leverage. For the sake of completeness, we report in Fig. 7a and b the detailed confusion matrices obtained for LIAR and Polifact datasets.5.3 Multimedia approach evaluationAfter the tuning phase described in previous section, we performed our experiments on PHEME dataset by leveraging CNN4 whose results are reported in Fig. 8a.Fig. 8Confusion Matrix for PHEME (a) and Polifact (b) datasetsFull size imageWe hypothesize again, that our results are quite better due to a fine hyper parameter tuning we performed, a better pre-processing step and the proper transformation. For the sake of completeness, we report in Table 13 and Fig. 8b the accuracy measures and confusion matrix obtained by CNN4 on Polifact datasets.Table 13 Evaluation measures for polifact datasetFull size table6 DiscussionsIn this paper we designed a benchmark framework in order to analyze and discuss the most widely used and promising machine/deeplearning techniques for fake news detection, also combining different features. More in details, our analysis is focused on the detection of fake news at early stage, that is when it is published on a social media. For this reason, we only

LIAR Dataset - Dataset - LDM - service.tib.eu

We report an excerpt of our setting steps on PHEME dataset (similar results have been obtained on the other datasets). We fixed the following parameter for CNN1: Number of Epoch = 10; Batch size = 32; Learning Rate = 0.001; Pooling matrix = 2x2; Dropout= 0.5; Input Shape = (128,128) and Activation Function= ReLU. We compared the performances on well-established evaluation measure like: Accuracy, Precision, Recall, F1 measure, Area Under Curve (AUC) (Flach & Kull, 2015) and the values reported in the obtained confusion matrices for each algorithm, i.e., True Positive (TP), False Positive (FP), True Negative (TN) and False Negative (FN).Table 8 Parameter tuning for pheme datasetFull size tableIn order to try to improve the accuracy we changed for CNN2 the learning rate to 0.001 and the activation function as sigmoid. The results showed a 20% improvement in accuracy. The latter is due to the new optimizer value, which combines the heuristics of both Momentum and RMSProp, and the different function used for the last layer, which performs better in binary-classification. To further improve the results, we implemented CNN3 by adding two additional layers that caused a further accuracy increase. Finally, we used a (3x3) kernel sizes that results in a lower number of weights and higher number of layers that turns out to be a more computationally efficient choice. Hence, we can conclude that 3x3 convolution filters will be a better choice.5 Experimental results5.1 Machine learning approach evaluationIn this section we reported the results of machine learning, deep learning and multimedia tailored approaches implemented in our benchmark.First, we performed a parameter optimization for the choosen Logistic Regression Algorithm, obtaining the results reported in Table 9.Table 9 Results of optimized logistic regression on politifact and LIAR datasetsFull size tableFor the sake of completeness, we report in Fig. 6a and b the detailed confusion matrices obtained for LIAR and Polifact datasets.Fig. 6Confusion Matrix for LIAR (a) and Polifact (b) datasetsFull size imageWe hypothesize that our results are quite better due to the fine feature selection task we performed, a better pre-processing step and the proper text transformation and loading.Fig. 7Confusion

LIAR Dataset – Machine Learning Datasets

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Distribution of classes of LIAR dataset (Complete dataset-

34(2), 76–81.Article Google Scholar Rubin, V.L., Chen, Y., & Conroy, N.J. (2015). Deception detection for news: three types of fakes. Proceedings of the Association for Information Science and Technology, 52(1), 1–4.Article Google Scholar Sharma, K., Qian, F., Jiang, H., Ruchansky, N., Zhang, M., & Liu, Y. (2019). Combating fake news: a survey on identification and mitigation techniques. ACM Transactions on Intelligent Systems and Technology (TIST), 10(3), 21. Google Scholar Shu, K., Cui, L., Wang, S., Lee, D., & Liu, H. (2019). defend: Explainable fake news detection. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining (pp. 395–405).Shu, K., Mahudeswaran, D., Wang, S., Lee, D., & Liu, H. (2018). Fakenewsnet: A data repository with news content, social context and dynamic information for studying fake news on social media. arXiv:1809.01286.Shu, K., Sliva, A., Wang, S., Tang, J., & Liu, H. (2017). Fake news detection on social media: a data mining perspective. ACM SIGKDD Explorations Newsletter, 19(1), 22–36.Article Google Scholar Shu, K., Wang, S., & Liu, H. (2019). Beyond news contents: The role of social context for fake news detection. In Culpepper et al. (2019). (pp. 312–320).Silva, R.M., Santos, R.L., Almeida, T.A., & Pardo, T.A. (2020). Towards automatically filtering fake news in Portuguese. Expert Systems with Applications, 146, 113199.Article Google Scholar Vosoughi, S., Mohsenvand, M.N., & Roy, D. (2017). Rumor gauge: Predicting the veracity of rumors on twitter. ACM Transactions on Knowledge Discovery from Data (TKDD), 11(4), 1–36.Article Google Scholar Wang, S., & Terano, T. (2015). Detecting rumor patterns in streaming social media. In 2015 IEEE international conference on big data (big data) (pp. 2709–2715). IEEE.Wang, W.Y. (2017). “liar, liar pants on fire”:, A new benchmark dataset for fake news detection. arXiv:1705.00648.Wang, Y., Yang, W., Ma, F., Xu, J., Zhong, B., Deng, Q., & Gao, J. (2020). Weak supervision for fake news detection via reinforcement learning. In Proceedings of the AAAI conference on artificial intelligence, (Vol. 34 pp. 516–523).Wu, K., Yang, S., & Zhu, K.Q. (2015). False rumors detection on sina weibo by propagation structures. In 2015 IEEE 31St international conference on data engineering (pp. 651–662). IEEE.Zhou,. A copy of the LIAR dataset from the Wang paper - Liar, Liar Pants on Fire : A New Benchmark Dataset for Fake News Detection. - dgauraang/liar-dataset

LIAR Dataset - Papers With Code

Analyze news’ content with the aim to identify fake news, as also made in Gravanis et al. (2019); Reis et al. (2019) and Silva et al. (2020), without considering temporal and user-based features, that are typically used for contextual fake news detection (Nguyen et al., 2020; Wang et al., 2020).We performed our analysis on three dataset (an unbalanced large dataset (FakeNews) as well as two other smaller ones (PHEME and LIAR)). Firstly, a comparison among different machine learning models (i.e. Random Forest, Decision Tree, SVC and Logistic Regression) is performed, which highlighted Logistic Regression as the best model in terms of efficiency and efficacy measures. Compared with other models, Logistic Regression offers different advantages such as interpretability, fast execution time and few parameters to be tuned. Successively, deep learning models (i.e. Convolutional Neural Networks and BERT) are compared. As easy to note in Tables 10 and 11, Google B.E.R.T obtained the overall best results because it performs word-level embedding on the basis of their context although it is complex to train. Finally, a multimodal strategy has been further investigated by combining content and multimedia analysis in order to perform a fake image classification. As can be seen in Table 13, this approach achieves the best results in terms of accuracy, precision, recall and F1 leveraging multimedia data.7 ConclusionFake news is a challenging task even though several techniques have been developed over time to mitigate their negative effects.In this work, a benchmark analysis of fake news detection using classical Machine Learning and Deep Learning approaches (both for texts and images) has been discussed. As shown in Section 5 traditional machine learning classifiers have still advantages and some drawbacks. First, these methods are very fast both during training and test steps for supporting real time analysis. On the other hand, these methods are not still able to detect words semantic meaning and context of the word picked up from a sentence obtaining low accuracy values.In turn, deep learning classifiers can automatically extract textual features and analyze semantic meaning of the words based on sentence context and images. Nevertheless, these neural networks are

liar Datasets at Hugging Face

Camera Trap atau kamera jebakan ialah teknologi yang umum digunakan dalam mengamati satwa liar. Satwa liar sendiri ialah seluruh hewan yang hidupnya di darat, air maupun udara dengan sifat masih liar.Apa Itu Camera Trap atau Kamera Jebakan?Berbagai Keunggulan dari Penggunaan Kamera Jebakan Penggunaan Fleksibel dan Efisien Pengamatan Terus-menerusPerolehan Data Relatif Akurat Petunjuk Pemasangan Kamera Jebakan untuk Memantau Satwa LiarMenempatkan di Lokasi yang Sering digunakan Satwa LiarMengkaji dan Menyelidiki Lingkungan Memanfaatkan Pengetahuan dan Saran LokalMempertimbangkan Logistik dan Waktu ketika Tahap Awal PersiapanMenguji Kamera Harga Camera Trap TerbaikKetika satwa hidup di alam liar, tentu aktivitasnya tidak terpantau oleh manusia. Oleh sebab itu, apabila seorang peneliti ingin tahu perilaku yang umum dilakukan satwa liar di habitatnya, perlu memanfaatkan teknologi.Adanya inovasi teknologi sangat membantu mengamati satwa liar tanpa perlu mengganggu maupun menyakiti mereka. Selain itu, keuntungan yang ditawarkan oleh peralatan tersebut juga cukup banyak.Apa Itu Camera Trap atau Kamera Jebakan?Kamera jebakan ialah jenis kamera yang ada tambahan sensor gerak dan sensor panas. Keduanya bermanfaat dalam merekam keberadaan satwa liar di suatu area tertentu.Sensor Camera Trap nantinya bisa aktif sendiri tatkala ada objek yang bergerak atau ketika suhunya berbeda dengan lingkungan area cakupan sensor. Jadi, Anda tidak perlu memotret sendiri satwa liar di habitatnya.Camera trapping sendiri ialah metode untuk memotret satwa liar saat peneliti tidak ada di lokasi. Metode ini sudah dipakai dalam penelitian ekologi selama beberapa waktu.Kamera jebakan sudah umum dipakai dalam mencari tahu keanekaragaman beragam jenis satwa, misalnya saja mamalia yang ada di suatu area tertentu. Alat ini juga bisa membantu mengetahui Indeks Kelimpahan Relatif (RAI/Relative Abundance Index) satwa.Terutama satwa yang bisa diidentifikasi secara individual dari tanda-tanda alami di satwa terkait, misalnya saja loreng pada harimau. Melalui alat ini, peneliti bisa membuktikan bahwa di suatu area ada aktivitas keseharian yang umum dijalankan oleh satwa liar.Camera Trap sangat umum digunakan dalam monitoring satwa liar. A copy of the LIAR dataset from the Wang paper - Liar, Liar Pants on Fire : A New Benchmark Dataset for Fake News Detection. - dgauraang/liar-dataset

Top Liar Datasets and Models

Yang ada di area tertentu dan dalam upaya konservasi. Sebab, teknologinya tergolong mudah dioperasikan dan tidak memerlukan banyak tenaga kerja. Sementara pemasangan kameranya umumnya dilakukan pada tempat yang kerap dilewati maupun dijadikan sebagai tempat singgah sementara oleh suatu satwa. Sehingga kameranya mampu menangkap gambar satwanya.Selain itu, pemasangannya juga perlu memperhatikan kondisi sekitar supaya tidak mengganggu aktivitas satwa liar. Sehingga kameranya juga bisa bekerja dengan optimal.Tidak terbatas pada pengamatan satwa liar, kamera jebakan juga dimanfaatkan dalam berbagai bidang lainnya. Misalnya saja studi sarang ekologi, deteksi spesies langka, penelitian terkait pemakaian habibat hingga pendudukan struktur bangunan manusia.Baca juga: Binocular: Teropong dengan Dua Lensa yang Beda dari MonocularBerbagai Keunggulan dari Penggunaan Kamera Jebakan Camera Trap sudah umum digunakan dalam mencari tahu keberadaan atau populasi satwa liar. Alasan penggunaannya ialah adanya beberapa keunggulan menarik di bawah ini.Penggunaan Fleksibel dan Efisien Anda bisa menempatkan kamera jebakan di mana saja. Misalnya saja di hutan untuk mengatai harimau. Anda juga dapat mengambil atau memindahkan kameranya kapan saja sesuai keperluan.Coba bandingkan saja dengan pengamatan satwa liar menggunakan kamera biasa secara manual. Anda perlu mengikuti pergerakan hewan secara sembunyi-sembunyi. Tentu saja hal tersebut bukan aktivitas yang mudah untuk dilakukan. Hewan juga tergolong peka terhadap gerak. Jadi, bisa saja hewannya langsung lari begitu merasa Anda mengawasi mereka. Sehingga akan sulit bagi Anda untuk mengamati aktivitas hewan.Sementara jika menggunakan kamera jebakan yang ukurannya kecil, sama sekali tidak akan mengganggu satwa liar. Mereka bisa melakukan aktivitas seperti biasa di habitatnya.Pengamatan Terus-menerusKamera jebakan mampu mengamati pergerakan satwa liar sepanjang waktu. Coba bandingkan dengan manusia yang melakukan pengamatan. Sudah pasti membutuhkan lebih banyak tenaga dan biaya untuk melakukannya.Perolehan Data Relatif Akurat Keunggulan lain dari Camera Trap ialah data yang dihasilkan relatif akurat. Sebab, datanya berupa gambar-gambar dari satwa yang terekam. Tentu datanya bukan hasil manipulasi.Karena datanya cukup akurat, penggunaannya sangat membantu dalam upaya konservasi