The document discusses spam detection using machine learning. It outlines the problems of fake reviews, malicious tweets, and false information online. Existing detection systems are described as linguistic-based, behavior-based, or graph-based, each with advantages and limitations. The proposed solution uses both behavioral and linguistic features to identify spam. These include early time frames, rating deviations, personal pronouns, content similarity, and burstiness of reviews. The implementation flow diagram and advantages of reducing spam through an accurate, statistics-based approach with less human interaction are presented.

1. Spam Detection Using ML

2. The problem
Fake reviews
- Most ecommerce
product reviews are
fake
- The user could be
paid to post a
review.
- Someone could
post a fake review
to malign the
company
Malicious Tweets
- Tweets could
contain links to
potentially harmful
websites
- Could be a phishing
tweet
False Information
- Spreading false
information against
a person
- Or a company
- Or to provoke
someone

3. Existing Systems
Linguistic Based
- Humans can
understand
language but
machines can’t
- Used in search
engines to predict
next words
- Two Unigrams and
Bigrams
Behavior Based
- Based on Metadata
- User creates a set
of rules
- Updating of rules
might be needed
over time
- User-dependent
and human
intervention needed
Graph Based
- Integrate into a
single graphical
representation
- Runs graph based
anomaly detection
algorithms
- Not reliable and is
challenging to
detect false
reviews

4. Solution
1. Behavioral Based Features
a. Early Time Frame
b. Threshold Rating Deviation
2. Linguistic Based Features
a. Ratio of First Personal Pronouns
and Exclamation Sentences
3. Behavioral Based Features
a. Burstiness of reviews by 1 person
b. Average of user’s negative ratio
given to different businesses
4. Linguistic Based Features
a. Average Content similarity and
Maximum content similarity

5. Implementation

6. Flow diagram

7. Advantages
Linguistic Based Graph Based
- Each and every data obtained is accurate.
- Spam Features are as a built in function.
- Less human interaction.
- It is statistics based approach.
- Supports review centric spam detection.
- Supports reviewer centric spam detection.

8. Impact
Reduction in Spam
Spam

9. References
https://www.ijrte.org/wp-
content/uploads/papers/v8i6/F1120038620.pdf