Searching for ideological divisions in Indian society using political Twitter conversations*

In fulfillment of an honors degree in Science, Technology, and International Affairs in the Georgetown University School of Foreign Service

Abstract

Over the past decade, the use of social media and online networking tools has become popular within the Indian public sphere, as well as by the Indian government. This research uses social network analysis in order to search for ideological divisions among users in online political Twitter conversations. The study first looks at the case of the Foreign Contribution Regulation Act, which has been used by the current administration to strip upwards of 9,000 NGOs of their ability to receive funding from abroad. And to test the results, the study also looks into the controversial issue of demonetization, which also ignited conversations within the Indian public sphere all across social media. Using social media analysis in order to chart divisions in Indian civil society when it comes to this legislation and the government’s activity, this study will extrapolate whether support or rejection of an act is tied to particular religious, political, or social group affiliations. And finally, this argument will conclude with suggestions for the U.S. State Department on how to effectively engage with targeted aspects of society by using social network analysis to extrapolate political divisions within a region. My hope is that the government uses insight into local cultural backgrounds and contexts to avoid offense in outreach. 

Social Media Analysis

            NodeXL

This study utilized a widely researched, open, and free Microsoft Excel extension called NodeXL Pro, produced by the Social Media Research Foundation (Hansen et. al 2010). NodeXL’s features not only collect the tweets but can manipulate the data to represent more easily a visual map of what the network looks like, as well as which nodes are more central to the network. The NodeXL extension automatically downloads tweets and data from other social media platforms through an API, or application-programming interface, and puts it directly into an Excel sheet, which can be altered based on how the user wants the network to be visualized. The tool includes information like the tweet or edge, the users or vertices, numbers of followers, in-degree, out-degree, overall graph metrics, and betweenness centrality, among other factors. Currently, the user can type in a search term into the search query and collect data on that topic currently up to a maximum of 18,000 tweets as old as a week per query by rules of the Twitter API.

This website only shows elements of the full thesis, as it is being edited and reviewed for publication. For more information contact Shalina with the email button on the home page.  Further, this site only looks at the FCRA study.  

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Dedication:

I dedicate this work to the amazing women in my family, who have spent their entire lives transcending societal boundaries: from my great grandmother, "Big Nani" who courageously crossed from Sindh to Bombay with her children and the clothes on her back, to my curious and intellectual aunts that always strove for independence, my Nani, who is just hilarious and endlessly selfless, and my own mother, who constantly pushes me to work hard and appreciate my education. 

Any information used from this site can be cited using:
 
Chatlani, Shalina Haresh. "Extrapolating Ideological Divisions in the Indian Public Sphere through Political Twitter Conversations." Thesis. Georgetown University School of Foreign Service, 2017. Shalina Chatlani, 24 Apr. 2017. Web. Shalinachatlani.com/research. 
Acknowledgments and Short Foreword

I’m grateful for the help and advising received throughout writing this thesis from my advisor, Irfan Nooruddin, his student and an expert in social media analytics, Jason Morgan, and Marc Smith from the Social Media Research Foundation, whose counseling was invaluable.

Investing in cultural sensitivity for myself and for the United States...

  

The summer before writing this report, my original research question was remarkably different. Originally, I sought to determine ways in which actors within India might leverage social media in order to reach out to the West, to collect data on these methods, and propose a more effective form of online engagement for the U.S. State Department. As the daughter of two first generation Indian immigrants that had extensively traveled to India in her past, I thought that I already maintained adequate insight into India’s population to write this thesis. After arriving in India and speaking to leaders in upwards of 20 NGOs in Mumbai, I realized that I really didn’t understand the society much at all. To my surprise, I discovered that these NGOs did not desire to or could not reach out to the West for engagement or funding purposes. After further investigation, discussions with prominent journalists, and meetings with social media experts in Mumbai, I discovered that the Foreign Contribution Regulation Act precluded many of the organizations in India from effectively reaching and receiving support outside India’s borders. Thus, my research question changed entirely. Instead of assuming that groups within India would of course want to reach out to a nation like the United States, I realized that I ought to actually understand the various social, political, and religious contexts that might inform whether groups within India would like to be active outside the nation or not. So, I developed a methodology that would allow me to actually see and not assume sentiments from members in the Indian public sphere. I propose that this type of approach is one that the U.S. State Department ought to take as well when it is reaching out to other nations in order to avoid offense, ignorance, and faulty assumptions directed toward the populations of other nations.

 
 

Background- FCRA

On June 3, 2014, India’s Central Intelligence Bureau delivered to the Prime Minister’s Office a disparaging report, detailing the efforts of foreign funded NGOs to “take down” Indian development projects (Intelligence Bureau of India 2014). The report, which was leaked to the media, paints a picture of nefarious foreign donors that “cleverly disguise their donations,” as humanitarian aid projects (TNN 2014).  The writers claim that their “identified” donors had infiltrated Indian development through NGOs, as a way to “build a record against India” and serve the “strategic policy interests of Western Governments.” The bureau points towards anti-nuclear, anti-coal, and anti-GMO movements within the nation as being a product of covert Western intrusion, intended to derail India’s economic progress—with a stated GDP impact of 2-3% per year (TNN 2014). Whether or not the information in the report could be determined true, it is clear that the nation’s government—under the recently sworn in BJP Prime Minister, Narendra Modi—is highly skeptical of NGOs and their ability to “shape policy debates in India.”      

Since 2014, the Ministry of Home Affairs under the BJP has used this act to investigate and strip over 9,000 NGOs of their licenses to receive foreign funding, citing a failure to meet financial reporting standards. The Times of India wrote that the specific number of NGOs denied licenses, according to the Ministry of Home Affairs, had actually risen to 20,000 out of India’s total 33,000 NGOs, after these groups had all allegedly failed to complete the proper reporting paperwork. And, several hundred more are still under investigation (Aghekyan et. al. 2016). Following these cancellations, several targeted groups and human rights watchdogs, like the United Nations, have spoken out against the legislation for constituting a potential human rights violation (Aravind 2017).

Methodology

Social Network Analysis

In order to observe where members of Indian society may cluster around their differences in opinion regarding the two aforementioned topics, this research tapped into social network analysis (SNA). The ubiquity of technology and its increasing accessibility has led to the development of countless online social networks and communities. SNA has been referred to before throughout this piece; but to offer clarification, SNA can be understood as a study of how relationships online are formed and what factors they form around. Or more formerly, social networks can be understood as a collection of “nodes,” that consist of “network members”  (Reis Pinheiro 2011). These nodes, or network centers (individuals, organizations, etc.), are connected through “links” or “edges,” which represent distinct types of relationships. In telecommunications, nodes such as customers, landlines, and phones can be connected through calls, texts, and e-mails. In the case of social media, the nodes, which are the users, pages, and organizations online, form relationships through follows, retweets, likes, or mentions, among other forms of connectivity.


In SNA, one can determine the strength of relationships by studying how many of these links exist and form between different nodes (Reis Pinheiro 2011). Moreover, connections between nodes can be analyzed as directed or undirected within the network graph. Directed includes relationships that are asymmetric where acknowledgment is not necessarily reciprocated, such as in the case of a generic sender delivering a message to a receiver; undirected connections do not distinguish between nodes this way, as they are information exchanges within closer relationships like marriage partners, co-workers, and friends (Yang, Franziska, and Zheung 2016).

Twitter data & NodeXL

It should be noted that the study consulted with Marc Smith from the Social Media Research Foundation (SMRF) on the methodology for the study to make sure that all of NodeXL’s features were utilized appropriately. The SMRF maintains the NodeXL Graph Gallery website where researchers can see numerous graph examples on real Twitter data. Users can download the “NodeXL graph options” which are a type of “recipe” for visualizing collected data based on a graph that has been picked out in the gallery. In other words, the individual can import the criteria for determining how visible or highlighted vertices and edges are--this can be helpful, especially for researchers that are new to NodeXL and need a base start for their visualization. The actual graphs uniquely produced for this study are available on the NodeXL Graph Gallery website and the recipe can be replicated to datasets others produce. The same “recipe” was applied for both studies.

Social media platforms cover an extensive share of communication activities within the World Wide Web, but this research will specifically focuses on data from Twitter. In their 2016 study on using Twitter for strategic foresight, Uhl, Kolleck, and Schiebel write that Twitter in its form as a “microblog,” is an effective data analysis tool “thanks to its functionality and the availability of appropriate software.” They identify functions and elements of the tool, which it makes it ideal for data collection and also easy to use for social network mapping. Among the many reasons identified, Twitter has a limitation of only 140 characters on each post, which means that a large dataset will be manageable and relatively consistent. The platform is also public and allows users to take advantage of an open application programming interface (API). This function grants the researcher access to valuable online information without needing a license. Furthermore, Twitter’s operating framework makes it ideal for the development of a social network map. Three main elements of the platform create connections between users; these are “replies to,” “mentions,” and “retweets.” These abilities show the way messages are spread throughout the platform, as well as how users may use the service to disseminate information among like-minded thinkers. Hashtags also help the user find specific subjects (Uhl et. al 2016).

Data preparation steps: 

Step 1: Cleaning up the data


When working with large datasets, it’s important to filter out any noise in the data that may affect the accuracy of the results. Particularly, when collecting data over long periods of time, one should make sure that there is no duplicate information. Before starting to manipulate the data in NodeXL into a graph format, tweets collected in each topic area were manually checked to make sure that the data was actually applicable. Then, the study used the “prepare data” option to merge, but not count, duplicate edges so that any duplicate tweets that were exchanged between the two same users would be eradicated.

Step 2: Calculating graph metrics for value-based filtering

 

NodeXL’s graph metrics option allows the researcher to calculate important features for vertices like in-degree and out-degree, eigenvector centrality, and betweenness, among other factors. These elements narrow down vertices to users that are active in online discussions and interacting with other users. After determining these characteristics, the study a feature in NodeXL called “value-based filtering,” which allows certain data that fall below a threshold to be filtered out of the results. 

 

Step 3: Applying group clustering algorithm 

Finally, in order to form the actual groups the study chose a group clustering option to show a division in communities. The research chose the Clauset-Newman-Moore group clustering option, because the algorithm is made for “detecting community structure,” and it is much more efficient than Girvan-Newman algorithm. The latter option takes a hierarchical approach, based on edge betweenness, while the Clauset-Newman-Moore algorithm is computationally more efficient (Rodrigues et. al. 201). Further, this model makes sure that there are many edges in the communities, and only a few edges between the individual groups, in order to really showcase the divisions (Clauset, Newman and Moore 2004). To understand the mathematical model behind the Clauset-Newman-Moore algorithm refer to the study from Clauset, Newman and Moore 2004.

NodeXL, From: Stateofdigital.com
 
Data Collection

The search on NodeXL was limited to "FCRA" to prevent a return that would have been very clearly skewed. Of course, when choosing terms to use in the search there will always be the risk of personal bias affecting the results. However, since the experiment is primarily being conducted to identify group clustering across ideological spectrum, a more generalized term to search was favorable. The sample size for the study included 6751 users with 10,145 edges between them. Once the graph was laid out, the quantity of groups was limited based on the number of vertices each of them had. Further, group 1 was eliminated because it was a group of isolates, or users unconnected to other users with only self loops. Therefore, the graph displays groups 2 through 15, all of which have more than 100 vertices in each of them. Group 2 is the largest with 788 vertices, while Group 15 is the smallest with 124 vertices. The sample size as represented within clusters on the graph is 4832 users with 7262 edges. After finding the clusters and visually laying them out, autofill columns were used to label each group with its most frequently used hashtags. In order to assess the homophily theory and see whether groups converged based on similar interests, each cluster was viewed more closely.

 
 

Results- FCRA

Looking at these connections, it seems that there are three types of divisions on how these communities are discussing the legislation. As demonstrated by the graph. The groups that are shown with their connections, as well as the hashtags, show that there is are affinities around ideas of nationalism, democratic freedoms, and religion. The way these groups are collecting appears to confirm the homophily theory as presented by Choi, Sang, and Woo Park, as well as conclusions put forth on community cluster formation by Smith, Rainie, Shneiderman, and Himelboim in their 2014 Pew Research Center study.

 

Conclusions:

  1. Groups appear to organize around their views of nationalism, democratic rights, and religion.

  2. There are more groups that appear to favor the consequences of the FCRA for reasons of religion and nationals, than to dislike them for reasons of democratic freedoms.

Interactive Graph

For a closer look at the graph and access to tweets go to NodeXL Graph Gallery and look up the Foreign Contribution Regulation Act. At this site, sentiment analysis and other features are available. 

Policy Implications

First the limitations...

While it’s important to understand the limitations in interpreting conclusions, social network analysis as a field has been heavily observed and offers a valuable tool for understanding how individuals, users, activists, and other groups can cluster around ideas. This research is not proposing an overall conclusion about how individuals in the Indian public sphere may divide on certain issues, but offering valuable insight into how clusters may form. The number of users is thus enough to make conjectures on views of the nation’s populace; however, since social media are globalized platforms with users around the world, the research cannot offer definitive conclusions, but provide an extremely powerful tool. 

 

Social network analysis to enhance outreach abroad...

Within the vast amount of literature on the digital age and international politics, scholars have already extensively explored and affirmed that the Internet has completely transformed government-citizen relationships, interactions between nations, and national security concerns (Simmons 2013) (Sikkink 2009). Kahler goes further than Grewal in saying that “networks” have emerged as the “dominant social and economic metaphor” in the post 1980s world. While cross-border networks aren’t really new, he writes that the technological change, economic openness, and demands for transnational political collaboration,” have expanded online networks to the furthest corners of the world, and it has resulted in a challenge toward the conventional hierarchical structure of international political organizations (Kahler 2009). Globalization at the turn of the century had already changed concepts of nation-states and boundaries. He says that an analysis of networks and their “theoretical contribution should not be overlooked,” because they “offer a means to investigate” the relations between agents and structure in international politics (Kahler 2009).

But still relatively new to scholarly research, the field of digital diplomacy deserves greater attention. Both academics and government officials are now trying to determine the most effective methods of influencing populations abroad with the swipe of their keys, but have yet to fully understand the best methods of outreach over the Internet, given that the audience is widely diverse in ethnicity, belief systems, and cultural norms. Khatib describes in depth the U.S. Digital Outreach Team, and specifically the ways that the U.S. government has been able to use online communications technologies to directly engage with citizens in the Middle East as apart of ‘public diplomacy 2.0’ (Khatib et. Al 2012).  A variety of scholars have discussed strategies for effective diplomacy for decades and Cowan and Arsenault even developed a widely accepted model for diplomatic efforts that have been applied to digital diplomacy (Cowan & Arsenault 2008). And, many have written about the nuances of the U.S. government’s Statecraft Agenda and whether our outreach efforts to more harm than good (Ross 2011). But still, Dr. Pauline Kusiak wrote in 2012 for the Strategic Studies Institute Monograph that one of the biggest challenges facing the U.S. government, particularly given its Statecraft Agenda, would be fully understanding the diverse cultures, identities, and beliefs systems that dot an increasingly digitally connected world (Kusiak 2012). She says that if the U.S. cannot figure out how to tailor its public diplomacy efforts to better understand diverse populations before deciding to engage, it will no longer be able to maintain its influence abroad. This is especially applicable to digital diplomacy, in which messages to the world are transferred instantaneously and often without much overview. By observing how individual actors use digital diplomacy techniques, while working with populations on the ground, government regimes may better learn how to interact with populations abroad.

Where this research fits in...

It is evident that with the emergence of new media, the U.S. State Department must reorient its foreign policy strategies in order to accommodate the emergence of new and valid actors in the international system. Not only must the U.S. recognize that the public, and not just the government, holds power. It must also recognize the important, but significantly varied sociopolitical and cultural contexts of different regions in order to more effectively reach audiences that are critical to the government’s agenda. This research proposes social network analytics and sentiment analysis as a valuable prerequisite to targeted engagement with different regions. For instance, study 1 demonstrates that there are portions of the Indian public sphere that not only support the end of foreign funding for NGOs in the state, but actively believe in Western attempts to infiltrate the nation and send missionaries to “convert” Hindus in the nation. Without this fundamental understanding, that could only be garnered from studying real world statements from actual citizens in a nation, could the state department recognize the extent to which many aspects of the Indian public sphere rejects Western intrusion. By the same token, study 1 also demonstrates that there are factions of liberal citizens, students, journalists, and activists, that not only oppose the FCRA, but also excessive overreach by the modi government. When trying to determine which parts of Indian society to reach out to, this type of analysis will show the types of groups that might be more accepting of Western values.

Addressing the clear moral dilemma...

 

While social media and network analysis can serve as incredibly powerful tool of foreign policy and strategic outreach, it should not be abused. In looking at the background of the FCRA for example, it is evident that U.S. intrusion in India highly influenced sentiments of Western skepticism. In targeting populations within any region, those using social network analysis in the government or any other agency must recognize the consequences of perceived overreach by the public. The consequences of distrust may make the possibility of international engagement more difficult than originally anticipated. Rather, this type of methodology offers an opportunity for government officials to actively and more critically learn about, take into account, and address the various cultural, socioeconomic, and political backgrounds that may exist within another nation’s public, as well as strive to address them in a manner that is not offensive. Social media is a powerful tool for immediate and widespread engagement to target audiences, but it can also have a horrible side effect if used ignorantly. Further, it’s important to note that the responsibility of using such technology for ethical purposes is entirely on the shoulders of the user.

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