Influence of NaMo App on Twitter

Inﬂuence of NaMo App on Twitter
Shreya Sharma
M.Tech CSE
IIT Kanpur
shreyasa@iitk.ac.in
Supervised by:
Prof. Ponnurangam Kumaraguru
Prof. Amey Karkare

Why Social Media Analytics ?
● Approximately 51% of world’s population use social media
● On an average, user spend 2.5 hours daily
● Content generated by traditional media << Content generated by social media sites
● Used to spot trends and conversations and get information like sentiment, opinion, network formed
2
CSE, IIT Kanpur Inﬂuence of NaMo App on Twitter MTech Thesis Presentation

2019 Lok Sabha Elections
● Online election campaigns cost - 586 Millions
● 130 Million first time voters - 15% in 20s
● India’s first “WhatsApp Election” - 50,000 groups to spread campaign information
● First time :
○ Election Commission of India (ECI) issued social media guidelines
○ Candidates need to submit information about social media handles
● Most popular platform - Twitter
3
Campbell-Smith, Ualan and Bradshaw, Samantha, 2019, Global Cyber Troops Country Profile: India.
https://demtech.oii.ox.ac.uk/wp-content/uploads/sites/93/2019/05/India-Profile.pdf
1
1

NaMo App
● Centered around particular political party - BJP
● Extremely right leaning content
● Features
○ Activity points
○ Direct emails from prime minister
○ Built-in twitter-like network - My Network
○ Post, like, comment, and share
4

Content shared from NaMo App
5
Shared from
MyNetwork -
annotated with
via MyNt tag
Shared from
feed - annotated
with via NaMo
App tag

Motivation
After 2019 Indian Lok Sabha Elections, evidence were found demonstrating the growing capacity of cyber-troops, tasked with
manipulating public opinion online with geographically coordinated behaviours. Networks of paid workers and volunteers
disseminate sophisticated disinformation strategies across social media. NaMo App is created by one such IT firm which was
linked to fake facebook accounts.
● How does NaMo App affects the online conversation on more traditional networks such as Twitter?
● NaMo app consists of only biased content, so does it receives the same amount of engagement and the reaction on
Twitter?
6
Campbell-Smith, Ualan and Bradshaw, Samantha, 2019, Global Cyber Troops Country Proﬁle: India.
https://demtech.oii.ox.ac.uk/wp-content/uploads/sites/93/2019/05/India-Proﬁle.pdf
1
1

NaMo App -> Twitter
Users Content
Reachability?
Type, Amount, Time?
Part of Echo chamber
or Neutral users ?
Users - Who, Type?
Which party ?
Inﬂuence
Geographical
location? cluster?
Similarity with NaMo
content?
How much? Quantify?
7
Can we characterise the users that post
NaMo content on Twitter?
● Part of Echo Chamber?
● Certain states form a larger part ?
● Difference between users that post
using NaMo app and who post
similar content on Twitter
How much inﬂuence does NaMo
content have on Twitter?
How much content on Twitter is
contributed by NaMo?
What type of content shared from NaMo
App to Twitter ?
Reachability of NaMo content on Twitter in
comparison to its similar content?

Datasets
● Twitter Datasets:
○ 2019 Lok Sabha Election
■ 45 million tweets
■ Maintained by the PreCog group at IIIT Delhi / Hyderabad
○ Citizenship Amendment Act (CAA) protests
■ 1.2 million tweets
■ Collected by Neha Kumari, PhD at IIIT Delhi / Hyderabad
○ 2020 Delhi Election
■ 1.27 million users
■ Collected by the PreCog group at IIIT Delhi / Hyderabad
● NaMo App
○ Collected by Rohan Rajpal, B.Tech at IIIT Delhi / Hyderabad
● Used Twitter API for other Dataset
8

Echo Chamber during 2019 Lok
Sabha Elections

What is an Echo Chamber ?
● Environments in which users’ opinions, or political leanings are reinforced due
to repeated interactions with peers that share similar tendencies and attitudes
● For our research, Echo Chamber is - the political leaning of the content of user
agrees with political leaning of the content of users from network
10

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Network Analysis
Analyzed user’s followers,
following, and retweet
network
Content Analysis
Analyzed polarities of content
users produce and consume
Partitioning of Users
● To ﬁnd political afﬁliations
● BJP vs Others
Filtering of Users
● Tweeted at least 50 tweets during
2019 Lok Sabha Election and 2020
Delhi Election
● Used most frequent hashtags
● 6037 common users
Methodology to identify Echo Chamber

12
NivaDuck Dataset
● Twitter handles of Indian
political leaders
● 243 users in common
Hashtag Usage
● Utilised hashtag usage as the
proxy for political leanings
● Annotated political hashtags
trended during 2019 Lok
Sabha Elections as pro-bjp,
anti-bjp, pro-congress,
anti-congress and neutral
User Metadata (Proﬁle attributes)
● Twitter’s screen_name and their descriptions
● 474 users identiﬁed
Partition of
Users
Partitioning (identify political leaning) of Users

User Metadata - To partition the users
Screen_name:
● Keywords: bjp, modi, namo, congress, inc, cong
Description:
● Keywords: bjp, modi, namo, narendra modi, bhartiya janta
party, amit shah, amitshah, narendramodi, bjp4india,
rahul gandhi, congress, inc, priyanka gandhi, raga, shashi
tharoor
MODIﬁed_SKP BJP
Krish_BJP BJP
Shivam_INC INC
amitsoni_INC INC
Hardcore MODIJI fan. Former RSS
pracharak. District secretary BJP IT & Social
media Tirupattur DT. Tamil nadu.
BJP
Lawyer & Not associated with Congress in
any manner My tweets are my personal
views and still Rahul Gandhi is my leader
INC
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*Manually checked all usernames and descriptions to ensure correct leaning

Hashtag usage
Calculated following ratios for number of hashtags for each
user:
● pro bjp ratio - A / E
● pro congress ratio - B / E
● anti bjp ratio - C / E
● anti congress ratio - D / E
● percent of annotated hashtags - ( E / F )
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● number of annotated hashtags - E
● total hashtags (annotated and no-label) - F
● number of pro bjp hashtags - A
● number of pro congress hashtags - B
● number of anti bjp hashtags - C
● number of anti congress hashtags - D
After partitioning
● 4637 users politically annotated out of 6037 users
Users who have used utilised 0.1% of the annotated hashtags - allocated a leaning based on the ratio with the highest
value - assigned leaning to 3920 users

Methodology
Assigned
● Hashtags belonging to pro-BJP - score 1
● Hashtags belonging to anti-BJP or pro-Congress - score 0
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Calculated the following values for each user :
● Production polarity : The average of the scores of all the hashtags
● Production variance : The variance in the scores of all the hashtags
● Consumption polarity : The average of the scores of all the hashtags that user is consuming from its network
● Consumption variance : The variance in the scores of all the hashtags that user is consuming from its network

Polarity vs Variance
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Little variation in polarity of content of users with an average polarity close to 0 or 1
High variation
for users with
polarity close
to 0.5
Higher polarity - pro-BJP
Lower polarity - anti-BJP/pro-congress

Consumption vs Production
● BJP users - Red
● Other users - Blue
● Most users are producing and
consuming content with similar
polarity
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Pro-BJP - Highly
dense
Pro-congress - spread
out

Methodology
● 6037 users - 4637 users politically annotated
● Gephi - Network analysis tool
● Types of Network created - Follow, Following, Retweet
● Node Size - Degree/connections
● Node Color
○ BJP - Purple
○ Other (Anti-BJP, Congress) - Green
○ Unknown - Orange
20

Network Analysis - Methodology
Step 1: Importing nodes and edges Step 2: Calculated graph modularity
Ref: Fast Unfolding of Communities in Large Networks
21

Network Analysis - Methodology
Step 3:
Applied Force Atlas 2 layout for visualization
It performs:
● Scaling: Scales expansion of graph
● Dissuade hubs: Stronger repulsive forces to opposing hubs
● Prevent overlapping of nodes
22
Step 4: Color nodes according to political afﬁliation

Follow Network
Community 1
Community 2
Community 3
% of users
Community # Pro-BJP Other Unknown
1 95.8% 0.3 3.9
2 8.8 53.4 37.8
3 9.8 12.8 77.4
23

Inﬂuence of NaMo App on
Twitter

Terminologies/Deﬁnitions
● Seed users: The users who post content using NaMo App on Twitter
● Auxiliary users: The users who post tweets with content similar to that of tweets posted via NaMo App but does not use NaMo App for
posting them
● Affected users: Seed users + auxiliary users
● NaMo tweets: Tweets posted using NaMo App on Twitter
● Non-NaMo tweets: Tweets similar to NaMo tweets but not posted using NaMo App on Twitter
25
Dataset Creation
● Collected tweets that have via MyNt or via NaMo App tag at the end
● Collected URLs of the images that are part of tweets that have via MyNt or via NaMo App tag

RQ 1.1 -
How much content on Twitter is contributed
by NaMo?

Methodology for Text Clustering
To get tweets similar to NaMo tweets:
● Removed words in the vocabulary with document frequency 1
● Removed tweets with length less than 5
● Performed K-Means clustering
● Only considered a match if euclidean distance of the normalized
vector from its cluster centroid was less than 0.45
27

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Phashes
pHash Extraction using
ImageHash
Pairwise
comparisons
pHash-based Pairwise
Distance
Calculation
Clusters and images
of medoids
Clustering and medoid
calculation
Methodology for Image Clustering

Example of a Cluster
29

Results of Clustering - Matched Content
For Text
● 4170 Non-NaMo tweets matched to NaMo tweets
● 20500 Non-NaMo tweets matched to NaMo tweets (for CAA Dataset)
For Images
● 4705 unique Non-NaMo images matched to NaMo images
30

Temporal Analysis
To get one-to-one mapping of NaMo tweets with its matched Non-NaMo tweets (tweets similar to NaMo
tweets):
● Calculated cosine distance of NaMo tweet with every Non-NaMo tweet
● Mapped a NaMo tweet to another Non-NaMo tweet with a maximum similarity score
● Compared the timestamps of NaMo tweets with Non-NaMo tweets
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Results of Temporal Analysis
After removing exact Non-NaMo tweets and considering only ﬁrst occurrence,
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First Occurence
(For 2019 Election Dataset, left with 940 tweets )
First Occurence
(For CAA Dataset, left with 2811 tweets)

RQ 1.2 -
What type of content is shared from
NaMo App to Twitter ?

Word Cloud (CAA Protests)
Tweets not posted using NaMo App
34
Observed:
● Terms focusing on CAB protests such as ‘protests’,
‘indiaagainstcaa’, ‘anti’, ‘violence’, ‘ वरोध’, ‘muslim’,
‘protesting’, ‘students’, ‘police’, etc

Observed:
● Phrases in support of CAA bill such as
‘indiasupportscaa’, ‘caaclariﬁed’, ‘isupportcaa’,
‘provisions’, etc
● Terms related to nationalism, diversity or places such
as ‘pakistan’, ‘northeast’, ‘amritsar’, ‘afghanistan, ‘sikhs’,
‘hindu’, ‘linguistic’, ‘bangladesh’, ‘kashmiri’, etc
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Tweets posted using NaMo App
Word Cloud (CAA Protests)

Word cloud for auxiliary users (2019 Lok Sabha Election)
Tweets similar to tweets posted using NaMo App Tweets not similar to posts posted using NaMo App
36

Word cloud for seed users (2019 Lok Sabha Election)
Tweets posted using NaMo App Tweets not posted using NaMo App
37

Seed users:
Posted using NaMo App
● Pro-BJP phrases such as ‘deshkagauravmodi’,
‘indiavotesfornamo’, ‘aayegatomodihi’
● Terms related to development or schemes such as ‘beneﬁts’,
‘employment’, ‘aadhar’, ‘transparent’, ‘opportunities’,
‘betterbharat’, ‘empower’, ‘women’, ‘middleman’, etc
Not posted using NaMo App
● Mostly posting tweets containing terms related to criticising
other parties such as ‘gandhi’, ‘congress’, ‘ममता’, ‘पप्पू’, ‘क
े जरीवाल’,
‘दीदी’, ‘ वपक्ष’
Auxiliary users:
● Only posting about pro-BJP phrases such as
‘deshkagauravmodi’, ‘modiaanewalahai’,
‘indiawantsmodiagain’
Observations (2019 Lok Sabha Elections)
38

Conclusions of RQ 1
● Through temporal analysis, concluded
○ Tweets made using the NaMo App were already present on Twitter
○ No new information was coming from NaMo App
○ Users who uses NaMo App posted content on Twitter ﬁrst
● Through content analysis, concluded that
○ Affected users may be highly pro-BJP
■ Auxiliary users - only praising BJP
■ Seed users - apart from pro-bjp phrases, criticising other political parties
○ Tweets
■ posted from NaMo App - about policies, clarifying the govt, BJP’s beneﬁts
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RQ 2 -
How much inﬂuence does NaMo content
have on Twitter?

Hawkes Process
To show NaMo App is the cause of some posting activity on Twitter
● Event on one process can cause a response on other processes, increasing the probability of an event occurring on other
processes
● Can be used for different types of content posted on web communities
● Each web community has its own rates for posting the images as well as some inﬂuence due to other social media sites
● In our setting,
○ events -> posting of an image
○ processes -> web communities
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Results of Hawkes Process
∆t = 24 hrs NaMo Twitter
NaMo 0.25667107 0.1334780
3
Twitter 0.18027061 0.2567351
6
∆t = 48 hrs NaMo Twitter
NaMo 0.25685813 0.1402505
Twitter 0.54783613 0.2552396
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Weight Matrix - Inﬂuence matrix
● Amount of interaction from one process to another
● Weight value act as a rate parameter in the Poisson process
● Can be interpreted as the expected number of events caused on
a web community due to another web community
● For our research,
○ expected events - posting of an image from one platform
to another
∆t - Indicates that an event on one platform can cause an event on other
platform within the given time window
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Conclusions of RQ 2
● Observed similar values for all the time periods
● Twitter to Twitter - Retweeting activity on twitter can be attributed to the same image being shared on the same platform
● NaMo App to NaMo App - Value can be used to indicate that the same image posted by other user
● Inﬂuence:
○ Twitter to NaMo App > NaMo App to Twitter
● Content is ﬁrst shared on Twitter, then circulated or posted to NaMo App through other means
● Similar results were obtained through temporal analysis
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RQ 3 -
Can we characterise the users that post
NaMo content on Twitter?

RQ 3.1 - Are users part of an Echo Chamber on
Twitter?
● Hashtags belonging to pro-BJP - score 1
● Hashtags belonging to anti-BJP/pro-Congress - score 0
● Properties considered:
a. Production polarity
b. Production variance
c. Consumption polarity
d. Consumption variance
45

Polarity vs Variance
46

Consumption vs Production
47
Consumption and Production
polarity close to 1 - Pro-BJP users

RQ 3.2 - Do users of certain states form a larger
part of the user group than general users?
● Obtained locations of all seed and auxiliary users from their accounts
● Mapped city to state
● Calculated count of users for each state
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Fraction of Users (2019 Lok Sabha Election)
State Fraction of Affected Users Fraction of General Users
Uttar Pradesh 0.171811 0.140201
NCT of Delhi 0.156173 0.184110
Gujarat 0.128189 0.057137
Maharashtra 0.109671 0.149511
Rajasthan 0.062963 0.057609
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Affected user count 🔺
Affected user count🔻

RQ 3.3 - Is there any diﬀerence between seed
and auxiliary users?
● Created Word Cloud and calculated Odd-Ratios of twitter descriptions of Seed and Auxiliary users
● Higher Odd Ratio for a bigram or trigram for a given class indicates that the bigram or trigram has a close
relationship with that class
50

Word Cloud of twitter descriptions (twitter bio)
Seed Users Auxiliary Users
51

Odd Ratios of Bigrams (2019 Lok Sabha Elections)
Seed Users Auxiliary Users
52

Conclusions of RQ 3
● RQ3.1 - Affected users generate and consume tweets reﬂecting their pro-BJP ideology - part of an echo chamber
● RQ3.2 - Affected user group are more likely to belong to a heavily pro-BJP state
● RQ3.3 - Possibility of auxiliary users to hold relations with members of BJP party , whereas seed users are likely to
be supporters or lower-level worker of BJP and associated groups
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RQ 4 -
What is the reachability of NaMo content on
Twitter in comparison to non-NaMo content?

Correlation graphs normalized by followers count
55

CDF Graphs
56

Conclusions of RQ 4
● Content posted through the NaMo App may not have much reachability. Thus, does not
affecting the larger discourse on Twitter.
● Observed similar results for CAA dataset
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Conclusions
● Less inﬂuence of NaMo App on Twitter - not affecting the larger discourse and shows its inability to change the narrative on Twitter
● Association of auxiliary users with members of BJP party.
● Seed users (likely to be BJP workers) are responsible for cross posting content, were seen praising BJP party and demeaning other
political parties.
● Affected users are part of online echo chambers - unable to break into a wider and more diverse audience
● Limited reachability of the NaMo content on Twitter
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Future Work
● To identify users that BJP should target to increase its reach or inﬂuence through NaMo App
● A deeper content analysis to identify topics which are often cross posted
● If exists in future, one can incorporate party apps and its data to compare their visibility
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Limitations and Challenges:
● Naive Political Classiﬁcation
○ Considered only hashtags, and not the context of hashtags
■ Sarcasm tweets/Parity users - intent of tweet not aligning with intent of involved hashtags - may get allocated a
wrong political leaning
○ Classiﬁcation was done only for binary (BJP vs Others) on the basis of top 600 annotated hashtags - while this could have
also been done in multi class problem (Pro-BJP, Anti-BJP, Pro-Cong, Anti-Cong or other political party)
● Users with more neutral hashtags and less political hashtags - will get assigned a leaning on the basis of political hashtags only
● To get a better understanding of count of affected users- we could also compare these values by considering the tweets not from lok
sabha elections posted by users from these states
● Code-Mixed Data

Influence of NaMo App on Twitter

Influence of NaMo App on Twitter

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