Editing Messaging spam

'''Messaging spam''', sometimes called '''SPIM''',<ref>{{cite web|url=http://www.news.com/Spim,-splog-on-the-rise/2100-7349_3-6091123.html |title=CNET: Spim, splog on the rise |publisher=News.com |date= |accessdate=2013-07-07}}</ref><ref>{{cite web|url=https://www.newscientist.com/article/dn4822-spam-being-rapidly-outpaced-by-spim.html |title=Spam being rapidly outpaced by spim |publisher=New Scientist |date=2004-03-26 |accessdate=2013-07-07}}</ref><ref>[http://www.spamfo.co.uk/component/option,com_content/task,view/id,153/Itemid,2/ Spamfo: SPIM, your new spam]  {{webarchive|url=https://web.archive.org/web/20071021030758/http://www.spamfo.co.uk/component/option%2Ccom_content/task%2Cview/id%2C153/Itemid%2C2/ |date=October 21, 2007 }}</ref> is a type of [[spam (electronic)|spam]] targeting users of [[instant messaging]] (IM) services, SMS, or private messages within websites.

==Instant messaging applications==
[[File:SPIM on Telegram.png|thumb|Messaging spam on Telegram.]]
Instant messaging systems, such as [[Telegram (messaging service)|Telegram]], [[WhatsApp]], [[Twitter|Twitter Direct Messaging]], [[Kik Messenger|Kik]], [[Skype]] and [[Snapchat]] are all targets for spammers.<ref>{{Cite web |last=Agarwal |first=Shubham |title=Scientists found more than 1,000 AI spam bots trying to scam people and steal their social media profiles — and regulators can't keep up |url=https://www.businessinsider.com/social-media-flooded-spammy-ai-content-2023-8 |access-date=2023-10-17 |website=Business Insider |language=en-US}}</ref> Many IM services are publicly linked to [[Social media|social media platforms]], which may include information on the user such as age, sex, location and interests. Advertisers and scammers can gather this information, sign on to the service, and send unsolicited messages which could contain [[Scam|scam links]], pornographic material, malware or ransomware. With most services users can report and block spam accounts,<ref>{{Cite web |last=Kurt Knutsson |first=CyberGuy Report |date=2023-09-22 |title=How to protect yourself from social media scammers |url=https://www.foxnews.com/tech/how-protect-yourself-from-social-media-scammers |access-date=2023-10-17 |website=Fox News |language=en-US}}</ref> or set privacy settings so only contacts can contact them.

==Countermeasures==
* Many users choose to receive IMs only from people already on their contact list.
* In corporate settings, spam over IM is blocked by IM spam blockers<ref>{{Cite news |date=2023-06-02 |title=Tired of spam calls? Block promotional calls in some easy steps on Android, iOS phones |work=The Economic Times |url=https://economictimes.indiatimes.com/news/international/us/tired-of-spam-calls-block-promotional-calls-in-some-easy-steps-on-android-ios-phones/articleshow/100686894.cms?from=mdr |access-date=2023-10-17 |issn=0013-0389}}</ref> like those from [[Actiance]], [[ScanSafe]], and [[NortonLifeLock|Symantec]].
* IM providers like [[Kik Messenger|Kik]] have a "report user" button, which sends a chatlog to the [[Moderator (communications)|IM administrators]] who can then take action.

== Pornographic IM spambots ==
[[Spambot|Spam-bots]] often sign on to popular messaging services like [[Kik Messenger|Kik]]<ref>{{Cite news|url=https://www.tomsguide.com/us/porn-spam-kik,news-18908.html|title=Porn Spam Gets a Kik Out of You|date=2014-06-05|work=Tom's Guide|access-date=2017-08-18|language=en}}</ref> or [[Skype]] to spread [[Pornography|pornographic images]]. Often if the user responds they receive a [[URL]] inviting them to a [[Internet prostitution|private livestream]] that will ask them to enter [[credit card]] details for "age verification".

These bots target random usernames; this often results in minors receiving unsolicited pornographic images.

==On Windows NT-based systems==
[[File:Netspam.gif|thumb|Example of Messenger Service spam from 2007.]]

In 2002, a number of spammers began abusing the [[Windows Messenger service]], a function of Windows designed to allow administrators to send alerts to users' workstations (not to be confused with [[Windows Messenger]] or [[Windows Live Messenger]], a free [[instant messaging]] application) in [[Microsoft]]'s [[Windows NT]]-based operating systems. Messenger Service spam appears as normal [[dialog box]]es containing the spammer's message. These messages are easily blocked by [[firewall (networking)|firewall]]s configured to block [[Packet (information technology)|packets]] to the [[NetBIOS]] ports 135-139 and 445 as well as unsolicited [[User Datagram Protocol|UDP]] packets to ports above 1024.<ref>{{cite web |url=http://support.microsoft.com/kb/330904 |title=Messenger Service window that contains an Internet advertisement appears |publisher=Microsoft |accessdate=2023-12-01}}</ref> Additionally, [[Windows XP Service Pack 2]] disables the Messenger Service by default.

Messenger Service spammers frequently send messages to vulnerable Windows machines with a [[URL]]. The message promises the user to eradicate spam messages sent via the Messenger Service. The URL leads to a [[website]] where, for a fee, users are told how to disable the Messenger service. Though the Messenger is easily disabled for free by the user, this works because it creates a perceived need and then offers an immediate solution.{{citation needed|date=December 2013}}

==In opinion-based recommender systems==
{{See also|Recommender system}}In an opinion based [[recommender system]], an important concern is how to evaluate the user-generated reviews on the items. One of the purpose of this evaluation is to identify malicious or spam reviews. Poorly written reviews are considered helpless to the recommender system. However, even if a review is well generated, they can still be harmful to the recommender system by their biased prejudice to form an actual advertisement or slander towards a target item.

Current approach of spam detection methods includes analyzing the spam text and identifying the spam reviewers by their reviews and activities. For the first kind, a [[machine learning]] application on review text has been developed.<ref>Li, Fangtao, et al. "Learning to identify review spam." ''IJCAI Proceedings-International Joint Conference on Artificial Intelligence''. Vol. 22. No. 3. 2011.</ref> For the second kind, researchers use network motif analysis technique to identify spam reviewers by their recurring reviewing activity.<ref>O'Callaghan, Derek, et al. "Network analysis of recurring YouTube spam campaigns." ''arXiv preprint arXiv:1201.3783'' (2012).</ref>

==References==
{{Reflist|30em}}

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