Social media systems provide a platform for users to freely express their thoughts and opinions. Although this property represents incredible and unique communication opportunities, it also brings along important challenges. Often, content which constitutes hate speech, abuse, harmful intent proliferates online platforms. Since problematic content reduces the health of a platform and negatively affects user experience, communities have terms of usage or community norms in place, which when violated by a user, leads to moderation action on that user by the platform. Unfortunately, the scale at which these platforms operate makes manual content moderation near impossible, leading to the need for automated or semi-automated content moderation systems. For understanding the prevalence and impact of such content, there are multiple methods including supervised machine learning and deep learning models. Despite the vast interest in the theme and wide popularity of some methods, it is unclear which model is most suitable for a certain platform since there have been few benchmarking efforts for moderated content. To that end, we compare existing approaches used for automatic moderation of multimodal content on five online platforms: Twitter, Reddit, Wikipedia, Quora, Whisper. In addition to investigating existing approaches, we propose a novel Capsule Network based method that performs better due to its ability to understand hierarchical patterns. In practical scenarios, labeling large scale data for training new models for a different domain or platform is a cumbersome task. Therefore we enrich our existing pre-trained model with a minimal number of labeled examples from a different domain to create a co-trained model for the new domain. We perform a cross-platform analysis using different models to identify which model is better. Finally, we analyze all methods, both qualitatively and quantitatively, to gain a deeper understanding of model performance, concluding that our method shows an increase of 10% in average precision. We also find that the co-trained models perform well despite having less training data and may be considered a cost-effective solution.
Processing & Properties of Floor and Wall Tiles.pptx
Content Moderation Across Multiple Platforms with Capsule Networks and Co-Training
1. Content Moderation Across Multiple Platforms
with Capsule Networks and Co-Training
Vani Agarwal
linkedin/in/vani-agarwal-04a02bb
b/
@VaniAgarwal9 fb.com/vani.agarwal30
Dr. Arun Balaji (Chair)
Dr. Ponnurangam Kumaraguru (Co-chair)
2. 2
Thesis Committee
◆ Dr. Rajiv Ratn Shah, IIIT Delhi
◆ Dr. Niharika Sachdeva, InfoEdge
◆ Dr. Arun Balaji Buduru, IIIT Delhi
◆ Dr. Ponnurangam Kumaraguru, IIIT Delhi
4. What is content moderation
4Ref: 11
Different platforms have certain
policies, if content does not
meet the guidelines than
moderation action takes place.
7. Challenges for content moderation
7
◆ Different needs of different
platforms
◆ Huge amount of content
◆ The way in which content
displayed differs for each
platform
8. Platforms struggling to moderate content
8
Human
Moderators
suffer PTSD
High
Turnaround
Time
Ref: 1
High Cost
9. Research Aim
Given posts P = p1
,p2
,. . . ., pk
from domains D = D1
,D2
,. . . .,
Dn
, find a subset of posts which should be flagged for
moderation
9
INPUT OUTPUT
List of items to
be moderated
P’ where P’ ⊆ P
MODEL
10. Contributions
◆ Comparison of different methods across multiple platforms
◆ Capsule Networks for Content Moderation
◆ Co-training to understand domain adaptability
10
11. Outline
◆ Data Collection
◆ Comparison of Methods
◆ Capsule Networks
◆ Co-training for Domain Adaptation
◆ Conclusion
11
12. Data Collection
12
◆ Twitter, Quora, Wikipedia - public datasets
◆ Whisper - combination of public dataset and website
scraping
◆ Reddit - Collected data from subreddits
13. Data Collection of Reddit
◆ Subreddit r/creepy
▶ violent content
▶ Weak labels
◆ Subreddit r/pics
▶ normal content
▶ Manually checked 200 posts to see if they are problematic or
not
13
16. Wikipedia
“hey punk dont be deleting my stuff,
you know nothing bout the harly drags
so stay out of my shit you stupid nerd,
punk fag female thats all u, bitch”
16
19. Dataset Summary
19
Twitter Reddit Wikipedia Whisper Quora
Data
collection
strategy
1. Tweets
related to
protest,
riots[7]
Collected
data from
subreddits:
r/creepy
r/pics
1.Comments
of personal
attacks[3]
Hate
speech
related
posts[4]
and web
scraping
Insincere
questions
asked on
Quora[2]
2. Tweets
related to
Racism,
Sexism[1]
2. Toxic
comments
on talk
page[12]
20. Dataset Summary
20
Dataset Positive(1) Negative(0) Total Positive
class %
Text / Image
Quora 817 12244 13061 6% Text
Whisper 760 1720 2480 30% Text
Wikipedia1 647 5146 5793 11% Text
Wikipedia2 783 7195 7978 10% Text
Twitter2 1200 2000 3200 37% Text
Twitter1 3619 1052 4671 77% Text + Image
Reddit 2073 2598 4671 44% Text + Image
21. Data Pre-processing
Tweet - "nice to see that the top trending post by suriya
#TamilNaduBandh #Saithan are located around TamilNadu"
Anonymized Tweet - "nice to see that the top trending post by
<NAME> are located around tamilnadu"
21
Lower case
Remove hashtags, emoticons, punctuations
Named Entity Recognizer
22. Outline
◆ Data Collection
◆ Comparison of Methods
◆ Capsule Networks
◆ Co-training for Domain Adaptation
◆ Conclusion
22
23. Methods
23
Text
Models
Logistic Regression[5] LR_machina
Logistic Regression[6] LR_Badjatiya
Multi Layer Perceptron[5] MLP
Gated Recurrent Unit[7] GRU
Long short term memory[7] LSTM
Convolutional Neural Network[6] CNN
Capsule Network CapsNet
Fusion
Models
LSTM + (Object + Scene recognition) LstmFusion
CapsNet + (Object + Scene recognition) CapsFusion
24. Outline
◆ Data Collection
◆ Comparison of Methods
◆ Capsule Networks
◆ Co-training for Domain Adaptation
◆ Conclusion
24
25. Capsule Network Intuition
25Ref: 10
◆ It is not a human face.
◆ Capsule networks
understand spatial
orientation.
◆ Max Pooling loses
information.
27. Why CapsNet?
◆ Capsules output vector.
◆ Each Capsule decides which feature to
pass to higher capsule.
◆ Prominent features are transformed from
one Capsule to another using routing
protocol.
◆ This helps to learn semantic meaning of
text data well.
27
29. Experimental Design
- Evaluation parameters used
- Average Precision or Area under PR curve
- Macro F1
- 5 fold cross validations
- Grid search on various Hyper-parameters
- Train set - 80%
- Test set - 20%
29
30. Text All Model Results
30
Method Macro F1 Average Precision
GRU 0.6977 0.6983
LR_Badjatiya 0.7560 0.7260
LR_machina 0.6772 0.3805
CNN 0.6400 0.6960
LSTM 0.7076 0.7057
MLP 0.7300 0.6916
CapsNet 0.8254 0.7695
Performance in Twitter2 dataset
31. Text Model Results on all Datasets
31
Dataset Method Macro F1 Average Precision
Quora CapsNet 0.6959 0.9269
LSTM 0.6731 0.6560
Reddit CapsNet 0.7967 0.7373
LSTM 0.7306 0.7321
Twitter1 CapsNet 0.7953 0.7695
LSTM 0.8635 0.6748
Twitter2 CapsNet 0.8254 0.7695
LSTM 0.7076 0.7057
32. Text Model Results on all Datasets
32
Dataset Method Macro F1 Average Precision
Whisper CapsNet 0.9856 0.9783
LSTM 0.9816 0.9816
Wikipedia1 CapsNet 0.8361 0.9195
LSTM 0.7775 0.7413
Wikipedia2 CapsNet 0.8361 0.9195
LSTM 0.8098 0.7698
Average CapsNet 0.8244 0.8600
LSTM 0.7919 0.7516
34. Fusion Model Results
34
Dataset Method Macro F1 Average Precision
Twitter1 LstmFusion 0.6711 0.8381
CapsFusion 0.6968 0.8613
Reddit LstmFusion 0.7529 0.7566
CapsFusion 0.8141 0.8149
35. Takeaways from Capsule Network Model
◆ Capsule networks perform better than LSTM by 10.54% in
average precision.
◆ CapsFusion model performs better than LstmFusion by
5.2% in average precision.
35
36. Error Analysis
◆ Manual analysis of 50 instances marked wrong by LSTM but
correctly by CapsNet
◆ Findings:
▶ False Positives by LSTM / correctly classified by CapsNet
“ :I think ``YOU RACIST CUNT`` qualifies as defamation. diff. I didn't edit your
post, that was someone else.”
▶ False Negatives by LSTM / correctly classified by CapsNet
“` I had no interest in getting ``under your skin``. It's you and your fellow admins
who got under my skin. So well done. It doesn't matter anymore. — `”
36
37. Qualitative Analysis
Text - “Where are the activists and foot soldiers when k'tak
bleeds in silence.”
Label - Positive
DeepSHAP[8] results
37
38. Qualitative Analysis
Text - “The proud hero of kashmir! The hero of freedom
struggle.”
Label - Negative
DeepSHAP results
38
39. Outline
◆ Data Collection
◆ Comparison of Methods
◆ Capsule Networks
◆ Co-training for Domain Adaptation
◆ Conclusion
39
46. Co-training Results
◆ Just by augmenting with 20% samples we face reduction in
performance by 17% compared to a model trained on 100%
samples.
◆ As the percentage of Domain2 samples added to Domain1
increases, models performance improves.
◆ Therefore, if we only have a small amount of labeled data
co-training for domain adaptation is a viable option.
46
47. Outline
◆ Data Collection
◆ Comparison of Methods
◆ Capsule Networks
◆ Co-training for Domain Adaptation
◆ Conclusion
47
48. Conclusion
48
◆ We perform Multi-platform comparison for Content
moderation.
◆ Capsule Networks outperformed existing methods for
content moderation.
◆ Co-training for domain adaptation, a cost-effective solution
for annotating data.
49. Challenges, Limitation, Future Work
49
◆ Some datasets use weak labels,
▶ Future work: see if stronger labels perform better than weak
labels.
◆ Different platforms have different style of expressing content and
also have different moderation policies
▶ Co-training may not work if the policies of platforms do not
align.
◆ It was challenging to collect Reddit dataset
▶ Quarantined subreddits no longer available
◆ We plan to extend the work to video content on various platforms.
50. Acknowledgement
◆ Committee Members
◆ Indira Sen, GESIS
◆ Snehal Gupta, Asmit Kumar Singh, Shubham Singh
◆ Members of Precog
◆ Family and friends
50