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14 results for “spam”
Data and model for detecting spam activity on academic articles
<p>With the remarkable capability to reach the public instantly, social media has become integral in sharing scholarly articles to measure public response. This paper analyzes how Twitter bots interact with scholarly articles on the platform. Spamming by bots on social media can steer the conversation and present a false public interest in given research, affecting policies impacting the public's lives in the real world. In this paper, we determined whether bots are disseminating a given scholarly article based on analyzing the relationship between Twitter bots and several research factors. We developed and tested several supervised machine-learning classification models to tackle this problem. Through our analysis, we also identified that scholarly articles in health and human science are more prone to bot activity than other research areas.</p>
Dataset: Themes Cybersecurity ETF (SPAM) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
A shortcut to calculate SPAM limb-darkening coefficients
<p>This repository contains data files and other supplementary material associated with the manuscript entitled "A shortcut to calculate SPAM limb-darkening coefficients", published on the Research Notes of the American Astronomical Society (RNAAS).</p>
Spamming the regulator: Exploring a new lobbying strategy in EU competition procedures
<p>Original dataset associated with the article "Spamming the regulator: Exploring a new lobbying strategy in EU competition procedures". Covers all 108 final decisions in EU merger procedures with a Phase II investigation (Council Regulation 139/2004) of which a public version was available in the electronic registry of DG COMP in September 2020.</p>
DSS SS20 UE1 Spam text message dataset
<p>This is a dataset containing text messages which are labelled as HAM if they are valid, or SPAM if they are unwanted/pernicious. It is originally taken from Kaggle: <a href="https://www.kaggle.com/uciml/sms-spam-collection-dataset">https://www.kaggle.com/uciml/sms-spam-collection-dataset</a><br> <br> The dataset is used for exercise 1 of the data stewardship lecture at TU Vienna in the summer term of 2020</p>
Set of obfuscated spam dataset by using LeetSpeak transformations
<p>The usage of LeetSpeak and other text hiding tricks is often used by spammers in the distribution of unsolicited contents. To evaluate deobfuscation techniques and their impact on spam content classification, we preprocessed several popular public datasets to partially obfuscate the text. The datasets transformed are:</p> <ul> <li>YouTube Spam Collection [2, 3] which is available on <a href="https://www.dt.fee.unicamp.br/~tiago/youtubespamcollection/">https://www.dt.fee.unicamp.br/~tiago/youtubespamcollection/</a>.</li> <li>a subset of YouTube Comments [4, 5] which is available on <a href="http://mlg.ucd.ie/yt/">http://mlg.ucd.ie/yt/</a>.</li> <li>CSDMC2010 which is available on <a href="http://csmining.org/index.php/spam-email-datasets-.html">http://csmining.org/index.php/spam-email-datasets-.html</a>.</li> <li>TREC2007 which is available on <a href="https://plg.uwaterloo.ca/~gvcormac/treccorpus07/">https://plg.uwaterloo.ca/~gvcormac/treccorpus07/</a></li> </ul>
Detecting Opinion Spams through Supervised Boosting Approach
<p>Product reviews are the individual’s opinions, judgement or belief about a certain product or service provided by certain companies. Such reviews serve as guides for these companies to plan and monitor their business ventures in terms of increasing productivity or enhancing their product/service qualities. Product reviews can also increase business profits by convincing future customers about the products which they have interest in. In the mobile application marketplace such as Google Playstore, reviews and star ratings are used as indicators of the application quality. However, among all these reviews, hereby also known as opinions, spams also exist, to disrupt the online business balance. Previous studies used the time series and neural network approach (which require a lot of computational power) to detect these opinion spams. However, the detection performance can be restricted in terms of accuracy because the approach focusses on basic, discrete and document level features only thereby, projecting little statistical relationships. Aiming to improve the detection of opinion spams in mobile application marketplace, this study proposes using statistical based features that are modelled through the supervised boosting approach such as the Extreme Gradient Boost (XGBoost) and the Generalized Boosted Regression Model (GBM) to evaluate two multilingual datasets (i.e. English and Malay language). From the evaluation done, it was found that the XGBoost is most suitable for detecting opinion spams in the English dataset while the GBM Gaussian is most suitable for the Malay dataset. The comparative analysis also indicates that the implementation of the proposed statistical based features had achieved a detection accuracy rate of 87.43 per cent on the English dataset and 86.13 per cent on the Malay dataset.</p>
Spam: Software for Practical Analysis of Materials - datasets for tutorials
<p>Datasets for <a href="https://ttk.gricad-pages.univ-grenoble-alpes.fr/spam/intro.html">spam tutorials</a>:</p> <ul> <li><strong>VEC4</strong> dataset from E.M. Charalampidou, E. Tudisco, S.A. Hall <ul> <li><a href="https://theses.fr/2011GRENI090">Experimental details available here: Charalampidou, E.M. (2011), "Experimental study of localised deformation in porous sandstones", <em>PhD Thesis</em></a></li> <li>This dataset is two x-ray tomography scans of a notched sandstone sample scanned before and after deformation in an external rig.<br> There is a rigid body displacement between the two scans that can be taken into account nicely with a registration.<br> Scans from Laboratoire 3SR, Grenoble.<br> </li> </ul> </li> <li><strong>M2EA05</strong> dataset from E. Andò, G. Viggiani and J. Andrade.<br> This data already used in the following papers for comparison to numerical simulations: <ul> <li><a href="https://doi.org/10.1016/j.jmps.2016.02.021">Kawamoto, R., Andò, E., Viggiani, G., & Andrade, J. E. (2016). Level set discrete element method for three-dimensional computations with triaxial case study. <em>Journal of the Mechanics and Physics of Solids</em>, <em>91</em>, 1-13</a></li> <li><a href="https://doi.org/10.1680/jgeot.18.T.030">Nadimi, S., Fonseca, J., Andò, E., & Viggiani, G. (2019). A micro finite-element model for soil behaviour: experimental evaluation for sand under triaxial compression. <em>Géotechnique</em>, 1-6</a></li> <li>This dataset is a series of x-ray tomographies acquired during the triaxial compression of a small sample of a "Martian Simulant" soil, performed in Laboratoire 3SR, Grenoble.<br> </li> </ul> </li> <li> <p><strong>c01_F10_b4</strong> dataset from O. Stamati, E. Roubin, E. Andò and Y. Malecot</p> <ul> <li> <p><em>This dataset belongs to a paper which is under review</em></p> </li> <li> <p>This is a 4-binned x-ray tomography of a small cylindrical sample of <em>micro-concrete</em><br> Scans from Laboratoire 3SR, Grenoble.<br> </p> </li> </ul> </li> <li> <p><strong>SandNX</strong> dataset from M. Milatz</p> <ul> <li> <p><em>The dataset is currently being analysed further</em></p> </li> <li> <p>See this publication for the experimental setup: <a href="https://doi.org/10.1007/s11440-020-00922-y">Milatz, M. (2020). An automated testing device for continuous measurement of the hysteretic water retention curve of granular media. <em>Acta Geotechnica</em>, 1-19</a><br> Data acquired on NeXT-Grenoble, a simultaneous Neutron and X-ray scanner at the ILL in Grenoble (<a href="https://dx.doi.org/10.5291/ILL-DATA.UGA-73">Experiment UGA-73</a>).<br> Neutron tomography has had a x0.7 downscale to bring it approximately in line with the pixel size for the x-ray tomography.<br> </p> </li> </ul> </li> <li> <p><strong>YehyaConcreteNX</strong> dataset from M. Yehya, A. Tengattini, E. Andò, F. Dufour</p> <ul> <li> <p><a href="https://doi.org/10.1016/j.nima.2018.02.039">Yehya, M., Ando, E., Dufour, F., & Tengattini, A. (2018). Fluid-flow measurements in low permeability media with high pressure gradients using neutron imaging: Application to concrete. <em>Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment</em>, <em>890</em>, 35-42</a></p> </li> <li> <p>Data acquired on NeXT-Grenoble, a simultaneous Neutron and X-ray scanner at the ILL in Grenoble (<a href="https://dx.doi.org/10.5291/ILL-DATA.UGA-25">Experiment UGA-25</a>).</p> </li> </ul> </li> </ul>
Spams meet Cryptocurrencies: Sextortion in the Bitcoin Ecosystem
<p>In the past year, a new spamming scheme has emerged: sexual extortion messages requiring payments in the cryptocurrency Bitcoin, also known as sextortion. This scheme represents a first integration of the use of cryptocurrencies by members of the spamming industry. Using a dataset of 4,340,736 sextortion spams, this research aims at understanding such new amalgamation by uncovering spammers’ operations. To do so, a simple, yet effective method for projecting Bitcoin addresses mentioned in sextortion spams onto transaction graph abstractions is computed over the entire Bitcoin blockchain. This allows us to track and investigate monetary flows between involved actors and gain insights into the financial structure of sextortion campaigns. We find that sextortion spammers are somewhat sophisticated, following pricing strategies and benefiting from cost reductions as their operations cut the upper-tail of the spamming supply chain. We discover that one single entity is likely controlling the financial backbone of the majority of the sextortion campaigns and that the 11-month operation studied yielded a lower-bound revenue between $1,300,620 and $1,352,266. We conclude that sextortion spamming is a lucrative business and spammers will likely continue to send bulk emails that try to extort money through cryptocurrencies.</p>
PSSC dataset: Improving spam detection in Persian SMS by providing a comprehensive dataset
<p>Text messaging is widely regarded as one of the most frequently used methods of text communication among mobile phone users. The affordability and widespread use of texting make it an attractive option for advertisers and spammers. Unfortunately, this has resulted in a large volume of spam messages received by users, lowering their overall satisfaction with mobile phone usage. A significant obstacle in the field of Persian SMS spam classification and removal is the lack of a sufficient and diverse database of both legitimate (ham) and spam SMS messages in Persian. To solve the issue of the limited database of ham and spam Persian SMS messages, in this research a comprehensive database was established by gathering 4,389 text messages from various sources and labeling them.<br><br><br>سالاری, محمدحسین و شایگان, محمدامین,1402,بهبود تشخیص هرزپیامک در پیامک های فارسی با ارائه یک پایگاه داده جامع,اولین کنفرانس ملی آنالیز داده ها,یاسوج,,,https://civilica.com/doc/1670746</p><p> </p>
Harvested Area and Yield data for China from the SPAM database
<p>Harvested Area and Yield data for China that was extracted from the SPAM database (V1.1).</p>
Transcriptomic analysis of RNA-sequencing data from nontransgenic and transgenic tau SPAM mice
GEO Series GSE198667. Mus musculus. 20 samples. Type: Expression profiling by high throughput sequencing.
Silent Progression Activity Monitoring - SPAM Study
ClinicalTrials.gov study NCT05650281. IPD Sharing: NO. Countries: 1. Publications: 0.
Ureteral Stent-related Pain and Mirabegron (SPAM) Trial
ClinicalTrials.gov study NCT02095665. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.