# Studying Online Political Behaviours as Rituals: A Study of Social Media Behaviour Regarding the CAA

Amit Kumar Kushwaha, Subhadeep Mandal, Ruchika Pharswan, Arpan Kumar Kar, P. Vigneswara Ilavarasan

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## To cite this version:

Amit Kumar Kushwaha, Subhadeep Mandal, Ruchika Pharswan, Arpan Kumar Kar, P. Vigneswara Ilavarasan. Studying Online Political Behaviours as Rituals: A Study of Social Media Behaviour Regarding the CAA. International Working Conference on Transfer and Diffusion of IT (TDIT), Dec 2020, Tiruchirappalli, India. pp.315-326, 10.1007/978-3-030-64861-9_28. hal-03744823

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Submitted on 3 Aug 2022

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# Studying online political behaviours as rituals: a study of social media behaviour regarding the CAA

Amit Kumar Kushwaha, Subhadeep Mandal, Ruchika Pharswan, Arpan Kumar Kar and P. Vigneswara Ilavarasan

Department of Management Studies, Indian Institute of Technology Delhi, New Delhi 110016, India
Kushwaha.amitkumar@gmail.com, subhadeep.mandal@dms.iitd.ac.in, ruchi1996pharswan@gmail.com, arpan_kar@yahoo.co.in, vignes@iitd.ac.in

**Abstract.** In a world of ever-increasing microblogs, the opinions, preferences, support, frustration, anger and other emotions of people regarding various events and individuals, surface in varied ways on social media. The purpose of this research is to find those hidden patterns in raw data, which can explain meaningful insights about its creation, the groups of people who created them and their sentiments which led to the generation of such data. Sentiment analysis has always been an effective methodology for discovering emotion and bias towards or against a situation, topic, thought or initiative and finding other meaningful insights from unstructured data. In this research, we attempted a type of document clustering wherein we attempted to classify the sentiments of the citizens of India as they micro-blogged their opinions, thoughts, views and ideas during the implication of the Citizenship Amendment Act (CAA) on the social networking site, Twitter. By analyzing the tweets of 13,000 twitter users during a specific timeline during which the discussion regarding the CAA was at its peak, we analyzed the sentiment of those twitter users by clustering their tweets (documents) into four sentiment groups with the help of Latent Dirichlet Allocation (LDA) which is an important tool for topic modelling in the domain of sentiment analysis. Using political ritual theory, the present paper examines the sentiments of people who tweeted during a protest in India. After the classification, our research also maps the online political behaviour of these 13,000 social media participants to the postulates of political ritual theory which is explained by previous research regarding the behaviour of physically co-existing political participants and also justifies this display of various sentiments regarding the CAA in the footsteps of political rituals.

**Keywords:** Sentiment analysis, LDA, CAA, twitter, social media, politics, political rituals, emotional energy.

## 1 Introduction

The Citizenship Amendment Bill (CAB) created a massive storm of supporters and critics both inside and outside the parliament when it was amended. It was passed in the Loksabha at the stroke of midnight with 311 votes in favour of the bill [1]. The
CAB is something which the party of the ruling government has been pushing to implement in the country. The CAB relaxes the eligibility criteria to be considered citizens of India for refugees from three countries, i.e. Pakistan, Afghanistan and Bangladesh.

This article involves the research on analyzing the sentiments of people who actively displayed online political behaviour on micro-blogging site, Twitter through tweets when the ruling government of India was focusing on implementing the Citizenship Amendment Act (CAA) through the Citizenship Amendment Bill (CAB). Tweets are micro-blogs or texts whose length is fixed upto 280 characters for all users [2]. Twitter also has the provision of ‘retweeting’ the tweet of a person which involves the action of sharing a user’s previously posted tweet on one’s own feed [3]. Social media (like twitter) plays an important role by letting individuals know what other people think about their opinions and thus lets individuals with common beliefs and interests, connect with each other on an online environment [4]. Thus, we considered the importance of both tweets and retweets in our work and extracted the tweets of 13,000 twitter users through the Twitter API. These users tweeted or retweeted content to express their support or protest related to the CAA and NRC. In this work, leaving out all other characteristics of the online behaviour of individuals on social media, we will only be focussing on their political behaviours.

Our analysis would reflect upon the postulates derived by Marx (2019) in the light of Randal Collins' ritual theory and the results would be proved to be in alignment with the philosophies mentioned in the postulates, modelled into the following research questions:

*RQ I.* Can political conversations be considered as rituals that can succeed or fail in generating positive emotions?

*RQ II.* Does the intensity or strength of political convictions keep increasing and decreasing over time with the intensity of the political rituals?

*RQ III.* Are political behaviours inputs to rituals and outcomes of rituals in a feedback loop?

*RQ IV.* Are political behaviours chosen and adjusted as such that resultant emotional energy is maximized?

## 2 Prior Literature

The literature review has been mentioned in the light of a brief explanation and understanding of political ritual theory and its components.

### 2.1 Political ritual theory and its components

To study how the citizens of a democratic society behaves on social media, it is utmost necessary to understand their variations in political behaviour and the factors affecting them. Thus studying rituals become utmost important, which are particularly
vigorous, focussed and coordinated social interactions which has the potential to bring like-minded people together through positive interactions and emotions [5]. This can be explained in depth by *Interaction Ritual chains* [6], which at its core, argues that the move of physical encounters generate emotional energy among individuals which acts as a driving factor for their motivation and such interactions lead to the infusion of positive emotions with belief, interest, identities and norms, which make the latter elements, salient factors in political behaviours [7, 8, 9]. Inequality and a political approach that strives to appeal to ordinary people who feel that their concerns are disregarded by established elite groups in political participation have added to a practical desire to better understand subjectivity of citizens of a democracy [10, 11, 12, 13] and this subjectivity has constituted to face-to-face encounters or rituals on a large scale [14, 15]. This comes in support through the psychological fact that humans subconsciously automatically align their behaviours in social interactions which comes with increased empathy, affiliation and willingness to cooperate [16, 17, 18]. Symbols such as names, slogans, colours, objects, flags etc. are integrative parts of rituals and they are what ties interaction ritual chains together and in which very basic actions are devoid of symbolism [19]. Practically speaking, political values and conviction often compete not only with economic interests but also with other values [20]. Cognitive ability and resonance increase the likelihood of a moral concern to be triggered subconsciously in a situation [21, 22]. Specifically, if experiences that cause an impact are repeated in interaction ritual chains, it is quite possible that unconscious moral intuitions are modelled by past social interactions [23]. Finally and most important, ritual theory can be associated to arguments about the basic role of affect in decision-making and judgement [24, 25, 26, 27] and a strong case about how emotions play a prime role in motivated political reasoning [28].

# 3 Topic Modelling through LDA

Topic Modelling is a probabilistic approach towards clustering of documents. Going from raw data to topics in the data is called *inference* in the topic model. Latent Dirichlet Allocation (LDA) is a topic model that generates topics based on word frequency from a set of documents. LDA is particularly useful for finding reasonably accurate mixtures of topics within a given document.

In this process, the first objective is to find out ‘k’ number of topics that best describes our data. A Dirichlet distribution would mean having a distribution over a multinomial distribution. A Dirichlet distribution is represented by the expression:

$$P(p|\alpha m) = \frac{\Gamma(\sum_k \alpha m_k)}{\prod_k \Gamma(\alpha m_k)} \prod_k p_k^{\alpha m_k - 1} \quad (1)$$

Where $\alpha$ represents variance and $m$ represents the mean.

## 3.1 Data collection

We collected the tweets of 13,000 users of the platform who expressed their political views regarding the CAA during a particular timeline when discussions about the
CAA was at its peak and also offline events such as protest and support rallies, demonstrations, public addressals by political influencers, riots, violence, clashes with police and mob lynching were soaring throughout the country. These offline events significantly affected the sentiments of the twitter users discussing these events online and their tweets in support and against the CAA reflected their sentiments as they discussed and expressed about the same. The timeline for the collection of these tweets were from Friday, December 20, 2019 till Wednesday, January 15, 2020.

## 3.2 Topic Modelling using LDA

Through Topic Modelling, we modelled our data into four sentiment groups by harnessing Latent Dirichlet Allocation (LDA) which is a probabilistic approach of Latent Semantic Analysis (LSA). The positive sentiments, denoted by 'P', denoted individuals (users and influencer) who acted and spoke in favour of CAA and 'N', denoted individuals (users and influencers) who acted and spoke against the CAA. This gave us four sentiment groups, namely: **PP** (Those who were **positive** in the beginning of the discussion & stayed **positive** till the end), **NN** (Those who were **negative** in the beginning of the discussion & stayed **negative** till the end), **PN** (Those who were **positive** in the beginning of the discussion & became **negative** at the end) and **NP** (Those who were **negative** in the beginning of the discussion & became **positive** at the end)

### Political behaviour in present timeline

Upon analyzing the present behaviour of the individuals, that is their online political behaviour supporting and opposing the CAA placed the individuals under analysis into 4 distinct sentiment groups. Out of the individuals analyzed, 1000 users were selected in group PP, 316 users in group NN, 394 users in group PN and 455 users in group NP. After the users were assigned to their respective groups through topic modelling, we also extracted 3 influencers by the number of cliques and follower-following difference for each of these groups separately. The distribution of users and influencers in these distinct sentiment groups according to the sentiment or polarity of their tweets are summarized and shown in Table 1.

<table><caption>**Table 1.** Distribution of users according to sentiments groups.</caption><thead><tr><th>PP</th><th>NN</th><th>PN</th><th>NP</th></tr></thead><tbody><tr><td>1000 users</td><td>316 users</td><td>394 users</td><td>455 users</td></tr><tr><td>3 influencers</td><td>3 influencers</td><td>3 influencers</td><td>3 influencers</td></tr></tbody></table>

Total users: 13000. Total users showing polarity change: 2165. Rest were neutral.
It is to be noted that out of all the 13,000 users analyzed who participated in the discussion on CAA, not all were expressing through polarity (positive and negative) sentiments. Only 2165 users showed a polarity of sentiments, whereas there was a large number of people who kept their views neutral and expressed themselves as 'free-thinkers', that is either they tweeted in a neutral manner, just expressing their general views on the CAA without supporting or opposing the initiative or they were initially positive and later on became neutral and vice versa or they were initially negative and with due course of time in discussions they became neutral and vice versa. The current work deals with only those cases where a polarity (positive or negative) in the sentiments is noted. A primary and very crucial part of our work involved the division of these citizens into 'users' and 'influencers' which was done by two different approaches. These approaches were the follower-following ratio and determining the number of cliques. Having said that, the 3 influencers identified from each sentiment group are listed in Table 2.

<table><caption>**Table 2.** Three influencers from each sentiment group.</caption><thead><tr><td>PP</td><td>NN</td><td>PN</td><td>NP</td></tr></thead><tbody><tr><td>@SwarajIndia</td><td>@iamnikhilnanda</td><td>@ImJaffarHussain</td><td>@TheSiasatDaily</td></tr><tr><td>@FactCheckIndia</td><td>@MRVChennai</td><td>@debarati_m</td><td>@PBNS_India</td></tr><tr><td>@iamakbarowaisi</td><td>@GabbarSanghi</td><td>@hindupost</td><td>@anilkohli54</td></tr></tbody></table>

In our analysis, the tweets were the list of documents which needed to be modelled according to their respective topics.

## Political behaviour in historical timeline

After getting the behavioural statistics of the population under analysis, we found out using topic modelling as to how many of these twitter users were maintaining polarities in their conversations and how many were being neutral, was neutral in the beginning or became neutral in the end.

Now the question that arises is that did these people display such positive and negative sentiment polarities only for the discussions regarding the CAA or did they show similar sentimental behaviour in the past too? Does their past behaviour and pattern of sentiment changes dictate their present behaviour in any way?

To find out this relationship, we randomly selected 2000 users from our present corpus of CAA users, went back in their timeline and extracted tweets which were made before discussions regarding CAA even started or discussions had just started
off. We focused only on tweets which reflected their political sentiments and opinions regarding a scenario. We identified 200 such tweets for each user and extracted them using Twitter API to build our new corpus of historical data of individuals who also participated in discussions regarding CAA. Our new corpus was having 4,00,000 tweets on which we conducted topic modelling again keeping positive and negative sentiments in polarities. We performed the same procedure and obtained 4 different sentiment groups from these 4,00,000 tweets, the groups being PP, NN, PN, NP, where ‘P’ and ‘N’ denoted positive and negative sentiments respectively and PP, NN, PN, NP represented the sentiment changes as mentioned earlier in the article. This new analysis of historical tweets gave us 273 users in the PP group, 831 users in the NN group, 435 users in the PN group and 444 users in the NP group. As before we again identified, 3 influencers each from all of the sentiment groups. The results are tabulated in table 3. LDA was again performed on these 12 influencers to identify the topics and for validation purposes.

<table><caption>**Table 3.** Distribution of users in sentiments groups according to tweets in their historical timeline.</caption><thead><tr><th>PP</th><th>NN</th><th>PN</th><th>NP</th></tr></thead><tbody><tr><td>273 users</td><td>831 users</td><td>435 users</td><td>444 users</td></tr><tr><td>3 influencers</td><td>3 influencers</td><td>3 influencers</td><td>3 influencers</td></tr></tbody></table>

Total users: 2000; Total Tweets: 4,00,000; Total users showing polarity change: 1983.
Rest were neutral

The tweets of the 2000 randomly selected users were subjected to LDA and the topics were obtained accordingly.

### 3.3 Relationship between past and present behaviour

In order to find the behavioural pattern across the political behaviours exhibited by this selected population, the grouping of sentiments or identification of topics through LDA had to be done on similar parameters. It is not necessary that positive and negative sentiments can be expressed only regarding events such as CAA. If we consider the discussion regarding CAA as a political event, sentiments can be expressed by users of social media regarding any other political discussion irrespective of the timelines in which they occured. Thus our study involved only politically relevant tweets of the users from present and historical timelines to analyze whether the polarities in sentiments that they are displaying in the present discussion, has also been displayed in their past/historic discussions regarding politically relevant events. That is, how does the other sentiment groups (PP/NN/PN/NP) of the present timeline relate to the same sentiment groups (PP/NN/PN/NP) in the past? To understand this behaviour in a simpler manner, the approach is demonstrated by a flowchart in Fig. 1.
<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Flowchart showing the approach followed to relate past political sentiment behaviour with present political sentiment behaviour.</figcaption></figure>

The findings of this analysis between the present and past political behaviours of the randomly selected 2000 users suggested that no matter with what sentiment polarity they began a political conversation in the beginning of a timeline in the past, or how many times they had a 'swing' of polarity regarding political sentiments in the past, the final sentiment with which they closed their discussions at the end of the timeline in past, was carried over to start the discussions regarding the CAA, i.e. with the very same sentiment or polarity, they started their political discussion in the present.

<table><caption>**Table 4.** The relation of past and present behaviour analysis gave rise to 8 sentiment groups.</caption><thead><tr><th>GROUPS WITH USERS</th><th>PAST BEHAVIOUR CORPUS</th><th>PRESENT BEHAVIOUR CORPUS</th></tr></thead><tbody><tr><td>Group 1 (67 users)</td><td>N P</td><td>P N</td></tr><tr><td>Group 2 (60 users)</td><td>P P</td><td></td></tr><tr><td>Group 3 (214 users)</td><td>N P</td><td>P P</td></tr><tr><td>Group 4 (130 users)</td><td>P P</td><td></td></tr><tr><td>Group 5 (174 users)</td><td>N N</td><td>N P</td></tr><tr><td>Group 6 (92 users)</td><td>P N</td><td></td></tr><tr><td>Group 7 (110 users)</td><td>N N</td><td>N N</td></tr><tr><td>Group 8 (82 users)</td><td>P N</td><td></td></tr></tbody></table>
If we consider the relationship in grouping the sentiments, we obtain a resultant of 8 sentiment groups which explain the relationship between sentiments in past and present timelines. The results of this relationship are tabulated in table 4. Although sentiment analysis on the past data returned us 4 sentiment groups and that on the present (CAA) data also returned us the same 4 sentiment groups, but the relationship between them is described by 8 different groups of sentiment relationships i.e. NP → PN, PP → PN, NP → PP, PP → PP, NN → NP, PN → NP, NN → NN, PN → NN

## 3.4 Tweet patterns and Hashtags

After the different sentiment groups of users were identified based on our CAA corpus, we analyzed if the tweet patterns and the frequency of tweets of these sentiment groups had any story to convey. Surprisingly not only did the analysis show some clear results but also showed some distinctive differentiation between the tweet patterns of these sentiment groups which also explained a lot about the background, political intellect and political conviction of the users of those groups. The results are displayed in a graphical plot in Fig. 2.

<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Different tweet patterns and frequency of the four sentiment groups</figcaption></figure>

The above finding shows that individuals (users and influencers) who were in the PP group i.e. they were positive at the beginning of the political discussion on CAA and stayed positive till the end were the most active regarding posting of tweets and retweeting others tweets on holidays and weekends among all the four sentiment groups. This proves that this group of people were highly determined in their initiative and made proper use of non-working days to spread their word, supporting their cause and successfully generated enough emotional energy which helped them establish significant differentiation with the other groups.

We also studied the hashtags (#) which are used to convey one's view or message regarding support or protest towards a person, event or situation through one or two words. Hashtags help other readers have a rough idea of the message that the tweet carries without even reading the tweet in first place. For example, one of the
hashtags found in our context, ‘#caanrcprotests’, helps a reader identify at first glance that the tweet is regarding any of the protest marches and rallies that are being organized to oppose the implementation of CAA. The detailed analysis of the frequency of hashtags for each of the sentiment groups is shown in Fig. 3.

<figure><img src="image_5.png" /><figcaption>**Fig. 3.** Use of common and also unique hashtags by the different sentiment groups.</figcaption></figure>

# 4 Discussion

From our analysis it was clearly observed that people in groups 1, 2, 3, 4 in Table 4 (a total of 471 users [50.7%]), started their positive support for CAA in the present scenario with the same polarity (i.e. positive) with which they ended a political discussion on a historical timeline. I.e.: the person exhibited **PP** or **NP** behaviour in a past discussion, and exhibited either **PP** or **PN** behaviour in present (CAA) discussion.

Same was the case with groups 5, 6, 7, 8 in Table 4 (a total of 458 users [49.3%]), where users ended with a negative sentiment in a past political discussion and also started with a negative sentiment in present (CAA) discussion. This behaviour falls in the light of our RQ I which answers if *political conversations can be considered as rituals that can succeed or fail in generating positive emotions*, because if past political rituals wouldn't have created enough positive emotional energy, the same positive emotional energy wouldn't have been carried over to political rituals in present scenarios.
This past-present relationship of behaviours also answers our RQ II, which asks if *the intensity or strength of political convictions keeps increasing and decreasing over time with the intensity of the political rituals*. The intensity of political convictions dies down after peak moments which are generated through rituals and stay low until reproduced. Such cycles of periodic excitement and boredom are generally exhibited in social movements [29, 30].

The insights obtained in Fig. 3 also answers our RQ I that people prefer discussing politics with like-minded people [31, 32]. It also points out to our RQ III, in the light of which, the online rituals regarding the display of support or protest on CAA, provided the cultural capital on social media and also the opportunity to perform political convictions with confidence and in a skillful way. This further concludes this postulate that *political behaviours are inputs to rituals and outcomes of rituals in a feedback loop*.

According to ritual theory, in the absence of acute needs, material goods are only appealing to the extent that they contribute to emotional energy. It is the case if access to and outcomes of rituals are linked to materialistic benefits such as memberships to expensive clubs, colleges, schools, hospitals etc. or if money making itself becomes an emotionally charged group symbol [33]. But the entire discussion that went online regarding the CAA through tweets, didn't have any personal materialistic gains or monetary benefits associated with it. The sentiment groups 'fought' for establishing their identities, to establish their beliefs and shared ideology that implementation of the CAA will be a good decision by the country and that they support the ruling government and that it should be imposed. In this situation, individuals chose between values, identities and materialistic elements and went for the one with most emotional energy potential. This brings us to answering our RQ IV which answers how *political behaviours are chosen and adjusted as such that resultant emotional energy is maximized*.

## 5 Conclusion

In terms of influencer-user relationship it can be concluded that there exists a population who gets influenced by an influencer, no matter whatever the event is. Let it be related to the Citizenship Amendment Act or other political discussions in the past, these users always showed strong support and gullible behaviour towards a particular set of influencer(s). The analysis results concludes to the postulates about an online political environment that *political conversations can be considered as rituals that can succeed or fail in generating positive emotions, the intensity or strength of political convictions keeps increasing and decreasing over time with the intensity of the political rituals, political behaviours are inputs to rituals and outcomes of rituals in a feedback loop and political behaviours are chosen and adjusted as such that resultant emotional energy is maximized*.
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