mobile SPAM messages

Examples of mobile SPAM messages in the following topics:

• Marketing Innovation Trends

  • Mobile marketing is marketing on or with a mobile device.
  • The most popular forms of mobile marketing include:
  • On average, SMS messages are read within four minutes, making them highly convertible.
  • While this has been fruitful in developed regions such as North America and Western Europe, mobile SPAM messages remain an issue in many parts or the world.
  • Mobile content can also be delivered via MMS (multimedia message service).

• Mobile Marketing

  • Marketing communications on mobile devices is generally carried out via text messages or applications.
  • One of the most popular forms of mobile advertising is text messaging.
  • Game mobile marketing provides additional opportunities for brands looking to deliver promotional messaging within mobile games.
  • Some of the major concerns around privacy include mobile spam, personal identification, location information and wireless security.
  • Industry bodies including the Interactive Advertising Bureau and Mobile Marketing Association have established guidelines to prevent SPAM messages and the practice of carriers selling member databases to third parties.

• Practical decisions in the email application

  • If the evidence strongly suggests a message is not spam, send it to the inbox.
  • If the evidence strongly suggests the message is spam, send it to the spambox.
  • If we used the guidelines above for putting messages into the spambox, about how many legitimate (non-spam) messages would you expect to find among the 100 messages?
  • In the spam filter guidelines above, we have decided that it is okay to allow up to 5% of the messages in the spambox to be real messages.
  • However, it will also fail to correctly classify an increased fraction of spam messages.

• Row and column proportions

  • Data scientists use statistics to filter spam from incoming email messages.
  • By noting specific characteristics of an email, a data scientist may be able to classify some emails as spam or not spam with high accuracy.
  • This corresponds to column proportions: the proportion of spam in plain text emails and the proportion of spam in HTML emails.
  • This information on its own is insufficient to classify an email as spam or not spam, as over 80% of plain text emails are not spam.
  • We would also see that about 27.1% of emails with no numbers are spam, and 9.2% of emails with big numbers are spam.

• Improving the set of variables for a spam filter

  • The variable could take value 1 if the sender of the message has previously sent messages flagged as spam.
  • A third indicator variable could flag emails that contain links included in previous spam messages.
  • Variables (2) and (3) are specially designed to flag common spammers or spam messages.
  • This suggests variable (1) would be very effective at accurately classifying some messages as not spam.
  • For what is the extremely challenging task of classifying spam messages, we have made a lot of progress.

• Digital Marketing

  • Email, text messaging and web feeds can also be classed as push digital marketing when the recipient has not given permission to receive the marketing message.
  • (This is also known as spam).
  • (This is also known as spam).
  • The modernization and mobility of the consumer has forced marketers to innovate, spawning the direct digital marketing concept.
  • With direct digital marketing addressability comes in one of three forms: an email address, a Web browser cookie, and a mobile phone number.

• Introduction to Logistic regression

  • These emails were collected from a single email account, and we will work on developing a basic spam filter using these data.
  • The response variable, spam, has been encoded to take value 0 when a message is not spam and 1 when it is spam.
  • Our task will be to build an appropriate model that classifies messages as spam or not spam using email characteristics coded as predictor variables.
  • While this model will not be the same as those used in large-scale spam filters, it shares many of the same features.

• Digital Marketing

  • Internet marketing also refers to the placement of media along many different stages of the customer engagement cycle through search engine marketing (SEM), search engine optimization (SEO), banner ads on specific websites, email marketing, mobile advertising, and Web 2.0 strategies.
  • Push digital marketing involves a marketer sending a message without the consent of the recipients, such as display advertising on websites and news blogs.
  • Email, text messaging, and web feeds can also be classed as push digital marketing when the recipient has not given permission for the marketer to send the marketing message.
  • (This is also known as spam. ) Push technologies can deliver content as soon as it becomes available and are better targeted to their consumer demographics, although audiences are often smaller, and the costs of creation and distribution are higher.
  • Push and pull message technologies can be used in conjunction with each other.

• Modeling the probability of an event

  • The outcome, Yi , takes the value 1 (in our application, this represents a spam message) with probability pi and the value 0 with probability 1 − pi.
  • In our spam example, there are 10 predictor variables, so k = 10.
  • While the information about whether the email is addressed to multiple people is a helpful start in classifying email as spam or not, the probabilities of 11% and 2% are not dramatically different, and neither provides very strong evidence about which particular email messages are spam.
  • Since the response variable takes value 1 if an email is spam and 0 otherwise, the positive coefficient indicates that the presence of "winner" in an email raises the model probability that the message is spam.
  • Summary table for the full logistic regression model for the spam filter example.

• Trends in Advertising

  • A recent advertising innovation is "guerrilla marketing", which involves unusual approaches such as staged encounters in public places, giveaways of products such as cars that are covered with brand messages, and interactive advertising where the viewer can respond to or become part of the advertising message.
  • This reflects an increasing trend of interactive and "embedded" ads, such as via product placement, having consumers vote through text messages, and various innovations utilizing social network services such as Facebook .
  • In the past, the most efficient way to deliver a message was to blanket the largest mass market audience possible.
  • With the Internet came many new advertising opportunities: popup, Flash, banner, popunder, advergaming, and email advertisements (all of which are often unwanted or spam in the case of email) are now commonplace.
  • In the last three quarters of 2009 mobile and internet advertising grew by 18.1% and 9.2% respectively.