AbstractApps downloaded by users are mostly based on the psyche of downloadingwell-rounded and efficiently working apps. These performance parameters areassessed by the general users by rating these apps on a scale of 5. The toprated apps are the first to appear while searching and sorting for the desiredapps.
However, these ratings are being tweaked and fraudulently misrepresentedto appear on the popularity lists to boost downloads. There is a collective nodamong the users to keep these dubious deeds of misrepresentation at check. Thisfraudulent representation of mobile app ratings will be discerned in this paperby detecting the leading sessions of the App at which the fraudulent ratingsare depicted. Secondly, rating, ranking and review based evidences are mined bymodelling Apps’ behaviours of the same using statistical hypothesis tests.Furthermore, all the evidences for the detection of the fraud are integrated byoptimization based aggregation method. The efficacy and the scalability of thedetection algorithm and the proposed system are validates by implementing thesame on real-life data of the Apps collected from iOS App Store.
IntroductionWith the advent of the wide spread practice of cellular mobiles withinternet connectivity that replaced the public switch telephone network (PSTN),the face of the functioning of humans across the globe has taken giant leapstowards advancements in the fields of communication and connectivity. Mobileapplications have become the lifelines of these very smart phones with internetaccess through mobile broadband. In 2008, the App Store released by Apple gavea drastic turn to how smartphones are used altogether with the intent ofwell-packages, downloadable apps on phones.
Since then, the mobile applicationmarket has exponentially multiplied faster than a beanstalk. With projectedgross annual revenue to surpass $189 billion by the year 2020, the populationof web developers has seen a huge rise in numbers. With so much collectiveenthusiasm in this field, the number of mobile applications in the play storehas shot up with fierce competitions among the app developers for higher numberof downloads. Like in any field, the bug of fraudulent projections of performanceshas bitten this domain as well with fake representation of top rankings of Appsby some App developers which dupes users into downloading their Apps. The faketop leader board positions are achieved by paying up for a bot farm orhuman/internet water armies that are hired to rate, rank and provide the saidApp with a better review.
Quite significantly, with 6.2 billion app downloadsin India in 2016, about 16.2% of the downloads showed some kind of fraud withIndia ranking 10th highest ranking country for app install fraudrate by Tune’s Accounting. Thus, this must be controlled to provide the userswith an authentic list of Apps for them to choose from and give a fair chanceto the Apps that genuinely appear on top of the App leader boards. To curtail this fraud, the proposed system detects ranking frauds thatoccur majorly during the leading sessions of the Apps and not throughout thelifecycle of the Apps. Leading sessions of the App lifecycle have the highestprobability of a red flag being noticed in the ratings. Thus these leadingsessions must be detected in the first module. Once, the leading sessions aretracked, the rating based evidences, ranking based evidences and the reviewbased evidences are extracted from the modelling Apps’ behaviours of rating,ranking and reviews by making use of statistics hypothesis tests.
Theseevidences will be aggregated using aggregation methods based on optimization.If the said evidences differ vastly from the historical performances of Apps interms of ratings, rankings and reviews, then there is an anomaly that must beaddressed for course correction in the App rankings. LiteratureSurvey: Severalresearch papers were referred in order to make this paper a well-rounded paperfor further reference in this field of assessment. Thereare majorly three categories into which the research work can be grouped into.
Firstly, web ranking spam detection detectsany incidence of web spamming. Web spamming is the procedure of raising particularweb pages by tweaking page ranking algorithms of search engines. A, Ntoulaspresented a range of heuristic methods to detect factors affecting spam on webbased on content to find heuristic methods.
Using spamicity, Zhou et al.proposed online link spam and spam detection methods. Secondly,online review spam detection: spamdetection of the online reviews. B.Spirin et al. did a survey that introduced many algorithms and principles inliteration for Web Spam Detection. Thirdly,Mobile App Recommendation: it lays emphasis on the algorithms and factorsaffecting them in recommending mobile application to users in ways of usingtarget marketing. Challenges Faced: Identifyingfraud ranking for Apps is a subject still under study.
We propose a system tofill the void a little in detecting this fraud. There are a certain challengesthat we face on doing so that are listed below.Firstchallenge, the ranking fraud does not occur all the time in the lifecycle of anApp. Hence, we need to detect the time when it happens leading to identifyinglocal anomaly instead of global anomaly. Second challenge is to possess scalability detectranking fraud certainly without the use of any basis information because manuallabelling of ranking fraud for each and every App is very difficult.
Finally,it is hard to catch and verify the evidences associated with ranking fraud dueto the volatile nature of rankings in the charts, which influences us todiscover contained fraud patterns of mobile Apps as evidences.