On the public citation graph
Spam Mobile Apps
0 signed · 73 inferred · view as graph
Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.
Is this your work? Sign in with ORCID and put your name on the
claims that are yours.
Reference health. See whether every reference in this work resolves to a
known work and none has been retracted — and get a dated CiteStamped
badge you can embed.
What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/978-0-387-21606-5openalex-snapshot
The Elements of Statistical Learning
inferredcites →https://doi.org/10.1007/978-3-319-24177-7_15openalex-snapshot
DexHunter: Toward Extracting Hidden Code from Packed Android Applications
inferredcites →https://doi.org/10.1007/978-3-540-39804-2_12openalex-snapshot
SMOTEBoost: Improving Prediction of the Minority Class in Boosting
inferredcites →https://doi.org/10.1007/978-3-642-40203-6_11openalex-snapshot
AnDarwin: Scalable Detection of Semantically Similar Android Applications
inferredcites →https://doi.org/10.1007/s10462-009-9109-6openalex-snapshot
A survey of learning-based techniques of email spam filtering
inferredcites →https://doi.org/10.1016/b978-012088469-8.50052-8openalex-snapshot
Combating Web Spam with TrustRank
inferredcites →https://doi.org/10.1016/j.peva.2007.06.014openalex-snapshot
Offline/realtime traffic classification using semi-supervised learning
inferredcites →https://doi.org/10.1016/s0004-3702(97)00043-xopenalex-snapshot
Wrappers for feature subset selection
inferredcites →https://doi.org/10.1109/72.788645openalex-snapshot
Support vector machines for spam categorization
inferredcites →https://doi.org/10.1109/icdar.2005.135openalex-snapshot
Image analysis for efficient categorization of image-based spam e-mail
inferredcites →https://doi.org/10.1109/icse.2015.50openalex-snapshot
AppContext: Differentiating Malicious and Benign Mobile App Behaviors Using Context
inferredcites →https://doi.org/10.1109/icse.2015.61openalex-snapshot
Mining Apps for Abnormal Usage of Sensitive Data
inferredcites →https://doi.org/10.1109/icsm.2015.7332474openalex-snapshot
How can i improve my app? Classifying user reviews for software maintenance and evolution
inferredcites →https://doi.org/10.1109/mc.2005.132openalex-snapshot
Leveraging social networks to fight spam
inferredcites →https://doi.org/10.1109/msr.2012.6224306openalex-snapshot
App store mining and analysis: MSR for app stores
inferredcites →https://doi.org/10.1109/msr.2013.6624001openalex-snapshot
Retrieving and analyzing mobile apps feature requests from online reviews
inferredcites →https://doi.org/10.1109/re.2014.6912257openalex-snapshot
How Do Users Like This Feature? A Fine Grained Sentiment Analysis of App Reviews
inferredcites →https://doi.org/10.1109/re.2015.7320414openalex-snapshot
Bug report, feature request, or simply praise? On automatically classifying app reviews
inferredcites →https://doi.org/10.1109/tsmca.2009.2029559openalex-snapshot
RUSBoost: A Hybrid Approach to Alleviating Class Imbalance
inferredcites →https://doi.org/10.1111/j.1467-985x.2004.298_11.xopenalex-snapshot
The Elements of Statistical Learning
inferredcites →https://doi.org/10.1145/1014052.1014062openalex-snapshot
A probabilistic framework for semi-supervised clustering
inferredcites →https://doi.org/10.1145/1017074.1017077openalex-snapshot
Spam, damn spam, and statistics
inferredcites →https://doi.org/10.1145/1135777.1135794openalex-snapshot
Detecting spam web pages through content analysis
inferredcites →https://doi.org/10.1145/1166160.1166191openalex-snapshot
Content based SMS spam filtering
inferredcites →https://doi.org/10.1145/1277741.1277813openalex-snapshot
Relaxed online SVMs for spam filtering
inferredcites →https://doi.org/10.1145/1321440.1321486openalex-snapshot
Spam filtering for short messages
inferredcites →https://doi.org/10.1145/2030376openalex-snapshot
Proceedings of the 8th Annual Collaboration, Electronic messaging, Anti-Abuse and Spam Conference
inferredcites →https://doi.org/10.1145/2184305.2184317openalex-snapshot
Identifying spam in the iOS app store
inferredcites →https://doi.org/10.1145/2382196.2382224openalex-snapshot
Using probabilistic generative models for ranking risks of Android apps
inferredcites →https://doi.org/10.1145/2504730.2504749openalex-snapshot
Rise of the planet of the apps
inferredcites →https://doi.org/10.1145/2568225.2568276openalex-snapshot
Checking app behavior against app descriptions
inferredcites →https://doi.org/10.1145/2568225.2568286openalex-snapshot
Achieving accuracy and scalability simultaneously in detecting application clones on Android markets
inferredcites →https://doi.org/10.1145/2591971.2592003openalex-snapshot
A measurement study of google play
inferredcites →https://doi.org/10.1145/2635868.2635869openalex-snapshot
Apposcopy: semantics-based detection of Android malware through static analysis
inferredcites →https://doi.org/10.1145/2636242.2636244openalex-snapshot
Predicting user traits from a snapshot of apps installed on a smartphone
inferredcites →https://doi.org/10.1145/2736277.2741084openalex-snapshot
Early Detection of Spam Mobile Apps
inferredcites →https://doi.org/10.1145/2950290.2950299openalex-snapshot
What would users change in my app? summarizing app reviews for recommending software changes
inferredcites →https://doi.org/10.3233/ida-2002-6504openalex-snapshot
The class imbalance problem: A systematic study1
inferredcites →https://doi.org/10.48550/arxiv.cs/0009009openalex-snapshot
Learning to Filter Spam E-Mail: A Comparison of a Naive Bayesian and a Memory-Based Approach
inferredcites →https://doi.org/10.5220/0002996201420151openalex-snapshot
DON’T FOLLOW ME - Spam Detection in Twitter
inferredcites →https://doi.org/10.5555/1293951.1293954openalex-snapshot
The class imbalance problem: A systematic study
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/978-3-030-24051-6_55openalex-snapshot
Fake Product Review Detection and Removal Using Opinion Mining Through Machine Learning
inferred← citeshttps://doi.org/10.1007/978-3-319-91947-8_14openalex-snapshot
Multimodal Language Independent App Classification Using Images and Text
inferred← citeshttps://doi.org/10.1007/s10664-019-09706-9openalex-snapshot
Towards understanding and detecting fake reviews in app stores
inferred← citeshttps://doi.org/10.1007/s11151-020-09754-2openalex-snapshot
Cheating in Ranking Systems
inferred← citeshttps://doi.org/10.1016/j.engappai.2023.105974openalex-snapshot
Semantic similarity for mobile application recommendation under scarce user data
inferred← citeshttps://doi.org/10.1109/access.2021.3075573openalex-snapshot
Fake Reviews Detection: A Survey
inferred← citeshttps://doi.org/10.1109/acctcs52002.2021.00063openalex-snapshot
Research on Spam Web Page Detection Based on Unbalanced Data Processing
inferred← citeshttps://doi.org/10.1109/indicon45594.2018.8987165openalex-snapshot
Evaluating Usage of Images for App Classification
inferred← citeshttps://doi.org/10.1109/lcn48667.2020.9314784openalex-snapshot
Security Apps under the Looking Glass: An Empirical Analysis of Android Security Apps
inferred← citeshttps://doi.org/10.1109/tmc.2020.3007260openalex-snapshot
A Multi-modal Neural Embeddings Approach for Detecting Mobile Counterfeit Apps: A Case Study on Google Play Store
inferred← citeshttps://doi.org/10.1109/tmc.2025.3550121openalex-snapshot
Detecting and Characterising Mobile App Metamorphosis in Google Play Store
inferred← citeshttps://doi.org/10.1142/s1469026822500237openalex-snapshot
Identification and Filtering of Web Spams Using a Machine Learning Method
inferred← citeshttps://doi.org/10.1145/3196398.3196412openalex-snapshot
Why are Android apps removed from Google Play?
inferred← citeshttps://doi.org/10.1145/3308558.3313427openalex-snapshot
A Multi-modal Neural Embeddings Approach for Detecting Mobile Counterfeit Apps
inferred← citeshttps://doi.org/10.1145/3473343openalex-snapshot
How Do Home Computer Users Browse the Web?
inferred← citeshttps://doi.org/10.1145/3701716.3717501openalex-snapshot
BERTDetect: A Neural Topic Modelling Approach for Android Malware Detection
inferred← citeshttps://doi.org/10.1155/2018/2410206openalex-snapshot
Towards Accurate Deceptive Opinions Detection Based on Word Order-Preserving CNN
inferred← citeshttps://doi.org/10.1155/2022/8621083openalex-snapshot
A Systematic Overview of the Machine Learning Methods for Mobile Malware Detection
inferred← citeshttps://doi.org/10.2196/17300openalex-snapshot
Content and Quality of Infant Feeding Smartphone Apps: Five-Year Update on a Systematic Search and Evaluation
inferred← citeshttps://doi.org/10.22214/ijraset.2022.48377openalex-snapshot
The Increasing Rate of Inappropriate Bangla Apps in Google Play Store
inferred← citeshttps://doi.org/10.3837/tiis.2018.10.026openalex-snapshot
Finding Rotten Eggs: A Review Spam Detection Model using Diverse Feature Sets
inferred← citeshttps://doi.org/10.48550/arxiv.1711.09181openalex-snapshot
Towards Accurate Deceptive Opinion Spam Detection based on Word Order-preserving CNN
Editorial review for independent work. ICSAC — the Institute for Complexity
Science and Advanced Computing — runs a structured editorial panel for researchers
publishing outside journals, and publishes on CiteStamp rails. Submission is free
and goes through ORCID: icsacinstitute.org/submit.