SPAM Detection. 4.1 Certain SPAM will be detected at a rate of 99.9% or above during each calendar month.
4.2 The SPAM detection rates do not apply to emails using a non-English or non-European language or emails sent to invalid mailboxes.
4.3 In the event that certain SPAM detection rates drop below 99.9% in any one (1) calendar month, following a request submitted by the Customer in accordance with the procedure detailed above in Clause 7 of the SLA, Censornet will credit the Customer with one (1) day’s Service Credit if the claim is approved.
SPAM Detection. Spam detection is important for all blog crawling services. This is especially important when using ping servers or allowing access to an arbitrary list of weblogs beyond a defined list. As a separate process to fetching, spam filtering is about identifying and stopping blog posts that should not be further processed and stored in the repository. Spam blogs (splogs) are an increasing problem when capturing blogs beyond a list of qualified weblogs. Splogs are generated with two often overlapping motives. The first motive is the creation of fake blogs, containing gibberish or hijacked content from other blogs and news sources with the sole purpose of hosting profitable context based advertisements. The second, and a better understood form, is to create false blogs that constitute a link farm intended to unjustifiably increase the ranking of affiliated sites [7]. There are several techniques for detecting Spam, and several freeware tools available such as xxxxxxxx.xxx. However most of these are too simple to be implemented in a weblog spider. Another technique would be to implement our own Spam-blog Detection, and three different techniques are described in [7]. Given a blog profile, we present three (obviously non-exhaustive) scoring functions based on the heuristics stated below, denoted by SF1 to SF3. Each of them independently attempts to estimate the likelihood of a blog being a splog. For the ease of discussion, each state tuple in a given blog profile b is denoted as ST. A blog profile consists of the blog's URL and a sequence of blog state tuples, each of which is denoted as ( t, N, p.spam_score).
SPAM Detection. As described above, our aim is to develop a component for filtering out non-interpretable and useless content. Our methods here focus on filtering out the so called ”low quality” content. A review of previous literature reveals various works using Twitter pre-processing steps for a wide range of different problem settings [3, 5, 4]. Within this work we focus on a component aimed at pre-filtering the Twitter data that is used by the sentiment and event detection algorithms. In related work, Xxxxxx et al. reported that there is an underground market in Twitter net- work [7] to influence user perspective either through advertisements or tweets by agents such as mobile application. In their study, they also reported that 77% of spam accounts are identified by Twitter within the first day of creation, and 92% of spam accounts within first three days of creation. The authors also made the observations that 89% of spam accounts have fewer than 10 followers and 17% of spam users exploit hashtags to make their tweets visible in search and trending topics. Surendra et al. [8] proposed a way to deal with tweet-level spam detection where they mainly focused on hashtags, in order to identify spam tweets and annotate tweets. In this regard they collected 14 million English tweets from trending topics and labeled all these tweets using the following 4 steps:
1. Heuristic-based tweets selection to search for tweets that are likely to be spam
2. Near duplicate cluster based annotation to group similar tweets into clusters and then label the clusters
3. Reliable ham tweets detection to label tweets that are non-spam (also known as ham)
4. EM-based label prediction to predict the labels of the remaining unlabeled tweets using an Expectation Maximization (EM) algorithm. Table 5 lists a few example tweets from the 14 million dataset. The aforementioned 14 million tweets were available in form of tweets ids. Thus, we used the Twitter Stream API to collect all tweets together with the available meta-information published in JSON format via the Twitter Stream API. This process yielded a collection of about 9.5 million tweets from the 14 million tweets. The remaining 4.5 million tweets were either private or removed and, thus, could not be collected. The resulting dataset contains around 2.5 million ham tweet, while the remaining about 6 million tweets are I’ve collected 12,293 gold coins! xxxx://x.xx/MXyllUOlZa #android, #an- droidgames, #gameinsight Spam What would you spend a...
