Spam Filtering
​ Uma Sawant, Megha Pandey, Sambuddha Roy
​
​ LinkedIn
Spam
Irrelevant or unsolicited messages sent over the Internet, typically to
large numbers of users, for the purposes of advertising, phishing,
spreading malware, etc.
Spam removal essential for good user experience
3
Outline of the talk
Challenges in spam
filtering
Man + machine
motif
Spam filtering @ LI
4
Outline of the talk
Challenges in spam
filtering
Man + machine
motif
Spam filtering @ LI
5
Fighting spam
Large network
Small network
Growth
❏ Cold start : no training data for
ML algorithms
❏ Manual curation is doable
❏ Small network is less lucrative,
not many sophisticated attacks
❏ Need for automation at scale
❏ Manually curated data == training
data for ML algorithms
❏ Highly lucrative for spammers,
more and cleverer attacks
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Types of spam
1. Offensive content
❏ Hate mail
❏ Nudity
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Types of spam
2. Monetary gain
❏ Money scam
❏ Romance scam
8
Types of spam
3. Drive traffic to external link
❏ phishing
Wrong sender
Misdirection
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Types of spam
4. Collection of personal data
❏ Email, phone, birthday
❏ Address book sale
Hi XXX, How are you? I hope you are fine. I came across your
profile and found it very impressive,so I just reach out to you
regarding an offer for a online business. Can I ask for your active
phone number and email address?
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Types of spam
5. Fake profiles with malicious
intent
❏ Connect with someone
that would not have
otherwise accepted the
connection
❏ Precursor to fraud
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Types of spam
6. Unprofessional content
❏ Puzzles
❏ Memes
❏ Jokes
Puzzles
Memes
Jokes
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Outline of the talk
Challenges in spam
filtering
Man + machine
motif
Spam filtering @ LI
13
Typical ML pipeline
❏ Collect training data
❏ Train algorithms
Labeled training
data
false
Test data
ML algorithms
true
14
Spam filtering : Man + machine
❏ Spam data distribution changes as spammers change tactics
❏ Human intervention and classifier retraining is important
Labeled training
data
Feedback loop
spam
User generated
content
Spam detection
algorithms
FP
Human
Review
ham
15
Spam filtering : Man + machine
❏ Spam data distribution changes as spammers change tactics
❏ Human intervention and classifier retraining is important
Labeled training
data
Feedback loop
spam
User generated
content
Spam detection
algorithms
FN
FP
Human
Review
ham
Member flagging
16
Outline of the talk
Challenges in spam
filtering
Man + machine
motif
Spam filtering @ LI
17
Data @ LinkedIn
❏ User profile (photo, description,
geo ...)
❏ User updates (posts,
comments, like, shares, ...)
❏ Emails
❏ Ads
❏ Network connections
❏ Groups
❏ ...
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Spam filtering signals : content + context
Context : who is the
Goal : classify a
post as spam or
not spam
author (Number and
types of connections,
previous posts etc)
Content of the post:
text and rich media
(image, video)
Context : how the post
propagates over the network
Context : which user segment
Context : nature and
quality of comments the
post generates
does the post engage
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Spam detection pipeline
author, geo,
connections
Classification of
contextual
signals
User generated content
spam
scores
text ,
rich media
spam
Content
classification to
detect spam
Prioritize detected
items for human
review
spam
scores
Human review
ranked
items for
review
ham
20
Spam detection pipeline
author, geo,
connections
Classification of
contextual
signals
User generated content
spam
scores
text ,
rich media
spam
Content
classification to
detect spam
Prioritize detected
items for human
review
spam
scores
Human review
ranked
items for
review
ham
21
Content Classification
Text Content
Multimedia Content
➢ Profanity classifier for short text:
• Detection of abuse, profanity and personal attacks
in text updates and comments
• Short to mid length text
• Prevalent use of non-dictionary words and internet
slang
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Content Classification
Text Content
Multimedia Content
➢ Profanity classifier for short text
➢ Blogs and articles classifier:
• Detection of objectionable content in blogs and
articles
• Larger length text documents
• Understanding of context and overall semantic
sense may be useful
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Content Classification
Text Content
Multimedia Content
➢ Profanity classifier for short text
➢ Blogs and articles classifier
➢ Scams and promotions classifier:
• Posted with malicious intent
• Money scams, unsolicited promotions
• Can be detected based on text content of the post
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Content Classification
Text Content
➢ Profanity classifier for short text
➢ Blogs and articles classifier
➢ Scams and promotions classifier
Multimedia Content
➢ Profanity classifier for user photos and
video clips:
• Detection of objectionable content in images and
videos
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Content Classification
Text Content
➢ Profanity classifier for short text
➢ Blogs and articles classifier
➢ Scams and promotions classifier
Multimedia Content
➢ Profanity classifier for user photos and
video clips
➢ Near de-duplication
• Detect close visual similarity to known bad content
• Uses image hashing techniques
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Content Classification
Text Content
➢ Profanity classifier for short text
➢ Blogs and articles classifier
➢ Scams and promotions classifier
Multimedia Content
➢ Profanity classifier for user photos and
video clips
➢ Near de-duplication
➢ Other ML Classifiers:
• Based on a variety of visuo-temporal and spatial
features
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Engineering constraints
❏ Latency
❏ Online
❏ Nearline
❏ Offline
❏ Respect member privacy
❏ Enhanced member experience (personalization)
❏ Precision vs recall (high cost of false positives)
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