Self-Breeding Fake News: Bots and Artificial Intelligence Perpetuate Social Polarization in India’s Conflict Zones The International Journal of Information, Diversity, & Inclusion, 7(1/2), 2023 ISSN 2574-3430, https://jps.library.utoronto.ca/index.php/ijidi DOI: 10.33137/ijidi.v7i1/2.39409 Self-Breeding Fake News: Bots and Artificial Intelligence Perpetuate Social Polarization in India’s Conflict Zones P. R. Biju, Government Brennen College, India O. Gayathri, Government College Madappally, India Abstract Studies have found that artificial intelligence (AI) bots and cookies automate fake news in zones of social conflict such as race, religion, gender, and class. In this background, this paper investigates whether fake news is automated with the social structure unique to India. The research collected campaigning activities of political parties and politicians on the Internet but was limited to a select number of Facebook profiles, websites, hashtags, and Twitter profiles during India’s 2014 and 2019 general elections. Politicians and political parties on Twitter, Facebook and other websites formed the contact points where empirical data were collected in the research design. By reviewing hashtags such as #Nationwantsrammandir; #NaamVaapsi; #RamMandir; #AntiNationals; #caste; and #Hindutva, as well as fake social media accounts; discussion forums; and profiles of followers of politicians, the paper corroborated that bots, AI, and trolls serve fake news in the conflict zones of India and some forces are using it to perpetuate social divisions based on caste, class, religion, gender, and region. This paper argues that automated social media accounts spread false information that likely polarizes social conflicts in India. Keywords: artificial intelligence; bots; fake news; self-breeding; social polarization Publication Type: research article Introduction ccess to truthful information and citizens' faculty to discuss matters of public concern with fellow citizens is an integral part of a democratic polity. In India, even though access to information and news has improved significantly recently, there is widespread concern about the quality of online news (Aneez et al., 2019). Social media manipulation is one potential source of the deterioration of online news quality and political discourse. The role of social media and fake news in polarizing India on a communal line is already testified (Rajgarhia, 2020). Manipulating online news is motivated by making money, spreading ideologies, and gaining status. Various tactics, such as forged documents, viral slogans, doctored photos, and so on, are deployed to achieve those objectives (Buchanan, 2020). However, a critical force of fake news is technological feasibility, which is least understood in the broad frameworks (Al-Rawi & Shukla, 2020; Kertysova, 2018). This paper analyzes fake news in India and the role of bots, artificial intelligence (AI), and cookies in automating fake news. The paper identifies automation of disseminating fake news on the Internet with a specific mechanism called self-breeding. The term used in the paper has two implications. First, it refers to the purposeful attempt to diffuse falsehood over a broad spectrum A https://jps.library.utoronto.ca/index.php/ijidi https://doi.org/10.33137/ijidi.v7i1/2.39409 Self-Breeding Fake News 2 The International Journal of Information, Diversity, & Inclusion, 7(1/2), 2023 ISSN 2574-3430, jps.library.utoronto.ca/index.php/ijidi/index DOI: 10.33137/ijidi.v7i1/2.39409 of the undecided public who consume information without validating the merit of the content. The second implication of the term is that it is the conscious attempt to spread unverified information by default using automata. This term involves a predetermined sequence of operations, which according to this paper, are performed by bots, cookies, and AI. Literature Review Amid accusations that fake news was used in the 2016 U.S. presidential campaign, Donald Trump was elected president (Bovet & Makse, 2019). Russian bots in Americans' Facebook and Twitter profiles, which spread biased news, were confirmed by an inquiry commission (S. Rep. 116-290, 2020). This issue was widely reported in world media; for example, The New York Times titled the phenomenon “The Fake Americans Russia Created to Influence the Election” (Scott, 2017). The same phenomenon was reported during the BREXIT campaign in the U.K. Eurosceptic Twitter users outnumbered and out-tweeted pro-Europeans in the EU Referendum campaign. Research papers confirmed this (Bastos & Mercea, 2019). Social media's contribution to political misperceptions was frequent in the U.S. presidential elections (Garret, 2019; Guess, Nyhan, & Reifler, 2020). The selective sharing of information that exaggerates politicians has become a commonplace strategy for political campaigning (Arceneaux & Johnson, 2013). On social media- based political advertisements, politicians disseminate false information about their own and opponents’ activities to attract an audience that may not have otherwise paid attention. Also, these politicians distribute misleading information about themselves. In this case, they exaggerate their behavior, making insignificant actions known to the public, and put excessive importance on their social roles, sufficient to deceive average citizens. There still needs to be a consensus on fake news in the literature. One survey of fake news literature argued that there is a typology of fake news: satire, news parody, fabrication, manipulation, advertising, and propaganda (Tandoc et al., 2018). Several studies similarly categorized fake news (Mulroy, 2019; Rubin et al., 2015; Verstraete et al., 2021; Wardle, 2017; Watson, 2018; Weiss et al., 2020). Fake news can be totally or partially false (Allcott & Gentzkow, 2017; Gelfert, 2018; Kalsnes, 2018; Potthast et al., 2017; Recuero & Gruzd, 2019; Rini, 2017; Tandoc et al., 2018). It may follow the media agenda and tries to distort and manipulate the truth (Canavilhas et al., 2019; Rini, 2017;). Here the point is that fakeness depends on the intent of the creator. Most authors consider fake news to be content that was created with the intent to mislead (Allcott & Gentzkow, 2017; Gelfert, 2018; Lazer et al., 2018; Meneses, 2018; Nielsen & Graves, 2017; Rini, 2017; Shu et al., 2017; Tandoc et al., 2018). The word "fake" gives a clue to the idea that there is an element of intention to deceive news consumers. Fake is associated with imitating the real (Fallis & Mathiesen, 2019; Gelfert, 2018). Fake news seeks legitimacy by imitating the real to manipulate the news consumer (Blokhin & Ilchenko, 2015; Lazer et al., 2018; Levy, 2017). The goal of fake news on social media is to go viral (Calvert et al., 2018; Meneses, 2018; Rini, 2017; Silverman, 2016). On the other hand, there is an element of massive reach in a short time; hence, trolling is a tool. Trolling and targeted dissemination of information contained in the form of fake news has transformed the Internet. Caste, religion, language, region, class, and other divisive factors contribute enormously to trolling (Biju, 2019). In the book Shame and Internet Trolling, George Caspar (2014) documented the Internet's use for hate trolls. In the book The Fine Art of Internet Troll Slaying, author Tobor Eichmann (2016) deals with the subject and https://jps.library.utoronto.ca/index.php/ijidi/index https://doi.org/10.33137/ijidi.v7i1/2.39409 Self-Breeding Fake News 3 The International Journal of Information, Diversity, & Inclusion, 7(1/2), 2023 ISSN 2574-3430, jps.library.utoronto.ca/index.php/ijidi/index DOI: 10.33137/ijidi.v7i1/2.39409 discusses how to manage and overcome the effect of Internet trolling. This literature pinpoints that trolling comes out of hate, and the Internet is now a new weapon for spreading unverified hate-filled materials. The idea that there is a neutral intention in fake news can also be disputed. The creator’s intention may be only financial, even if unintentionally, eyeing advertising revenue from online clicks. For example, Fallis and Mathiesen (2019) illustrate an example of young people who may unintentionally publish a fake story with the motivation to go viral. However, the outcome is that the teens generate money via clicks. Previous studies have approached the problem from different angles. Some writers treat creator intentionality as an attempt to mislead readers, while others consider creators’ content with pointed financial and ideological motivations (Allcott & Gentzkow, 2017). The coexistence of many possible meanings acquired by the term ‘fake news’ causes several authors to contest the use of the expression. One argument is that journalists and scholars misuse the terminology. Depending on various contexts, ‘fake news’ does not have a stable meaning. It serves as propaganda that can jeopardize democracy (Habgood-Coote, 2019). The European Commission's report prefers ‘disinformation’ (Directorate-General for Communication Networks, Content and Technology, 2018). ‘Disinformation’ covers a broader spectrum of fraudulent content, with a deliberate intent to mislead on various platforms (Ireton & Posetti, 2018). Hence, this research prefers to describe fake news instead of defining it. We understand fake news as online disinformation that can be totally or partially forged content, often created intentionally. The purpose of its creation is to deceive or manipulate a specific audience. Fake news has a specific format. This format imitates a news article or report to acquire credibility. It has an opportunistic structure, such as title, image, and content. The structure of the content attracts consumers of news. Fake news can persuade consumers to believe in falsehoods to engage users in the content for articles to obtain and shares. Therefore, in the end, the reward for fake news is higher advertising revenue, ideological gain, or both. Considering these descriptions, there are intentions, actors, and motives in creating fake news. The typical attributes of fake news are that it spreads fast, is doctored, is incorrect, is sensational, is intentional, is unverified, and goes viral. Regarding India's cultural context, these specifications differ from how fake news and disinformation may be perceived in the West. Some research has begun to enter the scholarly discourse from an Indian perspective. The works of Gautam Adhikari, the historian; Ramachandra Guha, the television anchor; and Sagarika Ghose (Overdorf, 2012) are the best starting points to investigate this connection in the Indian context (Adhikari, 2011; Guha, 2013). Guha (2013) identified five distinct types of hate emails illustrative of the Homo Indicus Hindutvawadi—a term used by Guha in the book. The use of the Internet for promoting Hindutva has been documented in the book The Virtual Hindu Rashtra by Rohit Chopra (2019). Swati Chaturvedi (2016), a journalist in India, investigated the nexus between social media and right-wing trolls in the book I Am a Troll: Inside the Secret World of the BJP's Digital Army. Scientific testing of the correlation between fake news and social media is in vogue. Some works (Flaxman et al., 2016; Shmargad & Klar, 2019) attest to how different organizations and political actors gain visibility by deploying partisan information in India through polemical works (Ganguly & Dwivedi, 2019; Jha, 2019; Sharma, 2019). Some literature in India that combines right-wing ideology with populism argues that diverse political players are gaining a https://jps.library.utoronto.ca/index.php/ijidi/index https://doi.org/10.33137/ijidi.v7i1/2.39409 Self-Breeding Fake News 4 The International Journal of Information, Diversity, & Inclusion, 7(1/2), 2023 ISSN 2574-3430, jps.library.utoronto.ca/index.php/ijidi/index DOI: 10.33137/ijidi.v7i1/2.39409 competitive advantage in the public domain by utilizing populist techniques in which largely rhetorical unedited information reaches a larger audience. Instead of educating people with facts and logic, such right-wing literature and ideas rely heavily on rhetorical strategies that appeal to people's emotions and convictions. Specifically, the use of the Internet by right-wing political players to disseminate irrational ideas and rumors to organize a support base in India's cultural spheres increases their widespread acceptance. Their preferred method of persuasion is thus the use of polemical works devoid of facts and science. Other scientific documentations also posit that actors on the broad political spectrum are using the Internet for partisan messaging, misinforming the public, distorting history, appealing to caste identity, and polluting news spaces on the Internet to their advantage (Chopra, 2019; Guha, 2013; Overdorf, 2012; Revati, 2018; Rushdie, 2013). In their survey about the spread of misinformation during elections, Social Media Matters and the Institute for Governance, Policies and Politics (IGPP) stated that one in two Indians received fake news on Facebook and WhatsApp before the general elections (PTI, 2019). More academic discussions of these issues and popular accounts about the Internet's role in fostering social divisions are needed. Research about the information universe and people's electoral decisions was minimally affected by media that primarily reinforces existing predispositions (Lazarsfeld et al., 1944). More recent research strongly implies that newspapers and cable news outlets can change people's voting behaviour (DellaVigna & Kaplan, 2007; Martin & Yurukoglu, 2017; Thaler & Sunstein, 2009). Small changes could disproportionately affect habits of information consumption that will have negative impacts. Partisan information sharing has already attained more comprehensive scholarship (Bozdag & van den Hoven, 2015; Jacobson et al., 2016; Kitchens et al., 2020). Previous writings on this method of social media broadcasting proposed the filter bubble effect (or echo-chamber effect), where people see the information they would like to see by default (Flaxman et al., 2016). However, the reach of this scholarship in India needs to be advanced. This paper is a contribution to that discourse. It is also necessary to contextualize fake news with automation or robots that perpetuate fake news. Fake news does not match editorial criteria and journalistic practices, regardless of the producer's intent (Michaelson et al., 2019). To this end, newspapers embed bots in their online articles to protect the integrity of their brand. Many types of malware infect end-user devices, enlisting them into a botnet. The device infects online communities with a Command and Control (C&C) center and performs automated activities under the C&C. Studies on the interplay between AI and fake news are abundant, which substantiates the fact that AI-driven solutions can spread false information without human intervention (Kertysova, 2018; Nazar & Bustam, 2020; Woolley, 2020). Bots are a short form for Software Robots, which indicates software programmes, including automated scripts and autonomous agents that perform actions according to pre-defined conditions (Farooq & Grudin, 2016; Lebeuf et al., 2017). Bots can perform the task they are given by their creators via a pre-written logic. Bots bridge the gap between human actors and the automation of actions (van Tonder & Le Goues, 2019). On the other hand, international literature processes directives and instructions to deal with AI-driven disinformation (Villasenor, 2020). Scientific works on automated detection and its various dimensions are in vogue (Conroy et al., 2015; Marsden & Meyer, 2019). The biases in AI solutions that detect and counter fake news are also finding scholarly attention (Lee et al., 2019). https://jps.library.utoronto.ca/index.php/ijidi/index https://doi.org/10.33137/ijidi.v7i1/2.39409 Self-Breeding Fake News 5 The International Journal of Information, Diversity, & Inclusion, 7(1/2), 2023 ISSN 2574-3430, jps.library.utoronto.ca/index.php/ijidi/index DOI: 10.33137/ijidi.v7i1/2.39409 This study identifies bots that play a vital role in messages that function in conflict zones unique to India. Previous studies establish a synergy between untruth and bots or automated social media accounts (Aldayel & Magdy. 2022). Social media bots can be used for several purposes that can create conflicts. Bots help enlarge a person's popularity, event, or movement and can influence the outcome of even democratic elections (Allcott & Gentzkow, 2017). Social media bots are automated programmes used to engage in social media with individual users on a massive scale. They behave autonomously, independent of a human actor and are primarily used in unfair and despicable ways. Many social media bots do not communicate using language at all. They perform interactions such as “following” and “likes.” A single person can manage several bots. The literature review found that several international studies have widely defined and described fake news. The role of the Internet in spreading disinformation has been found in several scientific studies worldwide. The literature on automation of dissemination or disinformation also attained scholarly inquiries internationally. However, the literature survey finds a gap in the literature regarding two critical points from an Indian perspective. First, there needs to be more literature about fake news and conflict zones in India. The second is that the automation of fake news perpetuates conflict zones in India. Within the background of the literature above, there are gaps about the automated social media accounts spreading false information that likely polarizes social conflicts in India. The present research tries to shed new insights. Research Hypotheses In India's zones of social conflicts, unidentifiable entities are leveraging fake news to perpetuate social divides such as caste, gender, class, religion, language, and regional variations. Even though these groups operate in secret, it is possible to identify them by recognizing patterns in their operational dynamics. There are parallels between false news and India's conflict zones, which certain sections attempt to use for their selfish advantage. The operational dynamics of fake news dissemination suggest that social media is the landscape of disinformation. In India, some of these falsehoods originate from automated social media accounts. They are a form of social bots that contribute to the nation's increasing social polarization regarding race, caste, gender, and other social structural factors. Methodology We collected political campaigning activities of political parties and politicians who maintain a social media presence. The field of study was limited to a select number of Facebook profiles, websites, hashtags, and Twitter profiles during India’s 2014 and 2019 general elections. Twelve politicians and four political parties on Twitter, the websites of four politicians and political parties, and three politicians and four political parties on Facebook formed the contact points in our research design, where we collected empirical data. We analyzed profiles and their traffic in trackalytics.com to discover metrics on followers, followings, tweets, likes, and comments. We also triangulated the data from Trackalytics against our random observation of activities, such as the number of posts, followers, likes, and most discussed topics. Fake social media accounts were collected from politicians’ and political parties' profiles. These were scrutinized to reference them with viral hashtags such as #Nationwantsrammandir, #NaamVaapsi, #RamMandir, #AntiNationals, #caste, and #Hindutva. This approach presumed that bots were active promoters of false political information. The followers of politicians and political parties were observed to find fake social media profiles, which we define as social media https://jps.library.utoronto.ca/index.php/ijidi/index https://doi.org/10.33137/ijidi.v7i1/2.39409 Self-Breeding Fake News 6 The International Journal of Information, Diversity, & Inclusion, 7(1/2), 2023 ISSN 2574-3430, jps.library.utoronto.ca/index.php/ijidi/index DOI: 10.33137/ijidi.v7i1/2.39409 accounts with no profile picture, activities and engagements, no followers, and primarily inactive1 . The activities were reviewed to explore how bots, cookies, and AI disseminated false content. This methodology presumed that bot activities were closely affiliated with fake profiles representing similar profiles across social media platforms. Hashtags were examined concerning the conflict zones of India, such as #religion, #caste, and #gender. A content analysis of hashtags was organized to evaluate the nature of engagements and the type of information shared through hashtags. Equal attention was furnished to examine the affiliation of inactive, fake profiles with hashtags. Data This paper investigated two core themes: One, fake news finds synergy with conflict zones in India, in which some forces are using it to perpetuate divisions such as caste, gender, class, language, and regional differences in India, thereby trying to gain political fortunes. Two, AI and bots are used to automate the dissemination of fake news, perpetuating social conflicts due to the social structure in India. The study's data points are evidenced in the social media profiles of select politicians and political parties. The data comes from a survey of Facebook, Twitter, websites, blogs, and hashtags of politicians and political parties. The data pertains to two general election periods: 2014 and 2019. Table 1, Table 2, and Table 3 represent the study’s inquiry fields. The tables are empirical data sources where the study discovered bot activity perpetuating social conflicts. Table 1. Politicians on Twitter SL No Politicians Followers in Million by October 2021 Political Parties Followers in Million by October 2021 1 Shashi Tharoor 8.1 Aam Aadmi Party 5.7 2 Aravind Kejriwal 23.2 BJP 16.7 3 Nirmala Sitharaman 4.4 Congress 8.4 4 Smriti Irani 11.8 Samajwadi Party 2.6 5 Rajnath Singh 20.7 6 Narendra Modi 71.9 7 Sambit Patra 5.1 8 Piyush Goyal 10.4 9 Amit Shah 27 10 Akhilesh Yadav 14.83 11 Mayawati 2.1 https://jps.library.utoronto.ca/index.php/ijidi/index https://doi.org/10.33137/ijidi.v7i1/2.39409 Self-Breeding Fake News 7 The International Journal of Information, Diversity, & Inclusion, 7(1/2), 2023 ISSN 2574-3430, jps.library.utoronto.ca/index.php/ijidi/index DOI: 10.33137/ijidi.v7i1/2.39409 12 Lalu Prasad Yadav 5.9 Table 1 shows the empirical sources of data for the study. Here, the presence of bots that multiply the online visibility of political actors has been investigated. These bots also disseminate materials that bring the concerned actor a favourable position in electoral visibility. Twitter profiles of 12 politicians and four leading political parties were investigated, with data collection extending to October 2021. The selection of the twelve politicians and four leading political parties is random because they need not be based on prefixed criteria, which may result in bias. We found some interesting evidence of bot activity in the Twitter profiles surveyed. A Twitter profile @Maryam89837825, which follows Prime Minister Narendra Modi, has yet to upload profile pictures, acquire followers, or post tweets. There are plenty like this; for instance, @GN3366, @Sunilsabal13, @Mechmagic2, and @abhishe36556828 do not have a profile picture, followers, or tweets that were recently created, yet they seem to increase the followers of the Prime Minister. However, there is more to this story, as scrolling down gives the clear impression that these profiles, which are probably fake, operate as a bridge, as a facilitator, and as a potential reservoir of something hidden in the architecture of the communication possibilities of the Internet. How do bots operate? Bots are social media accounts that self-operate, send messages, and predict and suggest links and information. This is done through cookies and AI, which are embedded in the programming software of the bots. The fan followings on the social media profiles of political parties are apparent indications of bot activity. The connection between social media bots and the propagation of misinformation has not yet been empirically established. After examining the political actors indicated in Table 1, the analysis verifies the likelihood that bots operate in fake news. Typically, bots are divided into sub-classes. We only discuss one portion. For instance, news polluters are likely a subclass. Content polluters are one subclass that diffuses malware and unsolicited content. Others employ an algorithm that generates material automatically and interacts with humans on social media. The inactive social media profiles devoid of images and content are likely social bots that work in the social media environment to disseminate false information for the benefit of political players. The same pattern can be traced here, too. Similar to inactive profiles following politicians, inactive profiles following political parties tend to exhibit the same pattern. For instance, the Twitter handles @leo36798 following the Congress profile, @Modiiiiii7 following the BJP profile, and @Amit69394171 following the Aam Aadmi Party profile have neither profile images nor tweets. Moreover, our analysis confirmed that these profiles were created in October 2021, and the relatively recent origins of these accounts indicate strong bot operation. Websites operate as a one-stop solution for fan followers where access to all the social media profiles and other sources of information about the party, leaders, and so on are found, which caters to a vast audience. Table 2 represents the websites of four politicians and five political parties. https://jps.library.utoronto.ca/index.php/ijidi/index https://doi.org/10.33137/ijidi.v7i1/2.39409 Self-Breeding Fake News 8 The International Journal of Information, Diversity, & Inclusion, 7(1/2), 2023 ISSN 2574-3430, jps.library.utoronto.ca/index.php/ijidi/index DOI: 10.33137/ijidi.v7i1/2.39409 Table 2. Politicians and Political Parties with Websites Politicians Website Political Parties Website 1 2 Narendra Modi Congress Omar Abdullah Bhartiya Janata Party Nitish Kumar Samajwadi Party Mamatha Banerjee Bahujan Samaj Party Aam Aadmi Party These websites act as one-stop solutions to almost all the social media profiles of the actors we have studied. Indeed, they function as a vast repository of information dissemination. Reading the websites and their pattern of information dissemination confirms that the users attracted to these sites come with complex information interests and needs. They are platforms representing the new phase of the information landscape shaping civic discourse in India. The story of Facebook is about more than just fans connecting with their favourite leaders. Table 3 represents the Facebook profiles of politicians and political parties selected based on the highest visibility and fan following. Table 3. Politicians and Political Parties on Facebook by October 2021 Politicians Followers in millions Political Parties Followers in millions Narendra Modi 46 BJP 16 Aravind Kejriwal 8.8 Aam Aadmi Party (AAP) 5 Shashi Tharoor 1.5 Congress 5 All India Trinamool Congress 1.3 As seen in Table 3, each politician profile represents differing strands of political ideology along the political spectrum and political party. We incorporated Trinamool Congress because, in recent years, the party has single-handedly raised a solid alternative to the dominance of the BJP in national politics. Thus, Trinamool Congress’ social media presence and the subsequent proclivity of its online followers to spread fake and partisan information needed to be placed in the discussion. https://jps.library.utoronto.ca/index.php/ijidi/index https://doi.org/10.33137/ijidi.v7i1/2.39409 http://cm.bih.nic.in/ http://www.samajwadiparty.in/ http://wbcmo.gov.in/contact.aspx Self-Breeding Fake News 9 The International Journal of Information, Diversity, & Inclusion, 7(1/2), 2023 ISSN 2574-3430, jps.library.utoronto.ca/index.php/ijidi/index DOI: 10.33137/ijidi.v7i1/2.39409 Table 4. Politicians and Political Parties on YouTube SL No Politicians Followers in Million by October 2021 Political Parties Followers in Million by October 2021 1 Narendra Modi 14.2 BJP 4.61 2 Rahul Gandhi 1.6 Congress 2.95 3 Aravind Kejriwal 478K AAP 4.48 After joining YouTube on October 26, 2007, Narendra Modi, the leader of the BJP, gained 14.2 million followers, the largest following compared to other players listed in Table 4. To date, Congress has 2.95 million followers on YouTube, a couple of thousand followers on Instagram, and a few thousand on LinkedIn. The BJP as an organization has 4.61 million followers, and AAP has 4.48 million followers on YouTube. BJP had an active social media strategy and a “Mission 272+”2 strategy on leading platforms like Facebook and Twitter in the 2014 general election. Aam Aadmi Party developed a social media policy like a thunderclap strategy while maintaining district and state-wide social media profiles. On Facebook, we found that pages thesanatanadharmis (Sanathana, n.d.) and AntiReservationIndia (Anti Reservation Movement, n.d.) cater to particular audiences interested in conservative politics. These pages prove whether fake news reinforces divisions such as caste, gender, class, language, and regional differences in India. The audience of these profile pages is extensively connected to the social media profiles listed in Tables 1, 2, and 3. Table 5 presents a random sample of phony Twitter handles found following the profiles of politicians and political parties listed in Table 1, as well as Twitter handles and hashtags that aggressively spread malicious content. These profiles and hashtags were selected after intense scrutiny of the profiles listed in Tables 1, 2, 3 and 4. Table 5. Survey of Hashtags and Profiles Where Bot Activity is Likely Strong Twitter Profiles Surveyed Hashtag 1 2 3 Fake Twitter Profiles Twitter Profiles where Fake Profiles with a Militant Nature are Active Activity Strong @imsumit99, @Internet_Hindus, @Damru, @68e11543967046d, @atulkumarawasth, @as_465, @SwamyBhakt, @68e11543967046d, @vhskerala, @vhs_karnataka, @vhsindia, @harishmohamed58, @psjangin, @tiwariumeshlkw, @yogeshjangid592, @imaakhyan, @nimi2biju, @MukeskK, #RamMandir, #RemoveMughalHistory, #Gaumata, #Prayagraj, #Ayodhya, https://jps.library.utoronto.ca/index.php/ijidi/index https://doi.org/10.33137/ijidi.v7i1/2.39409 https://www.facebook.com/thesanatanadharmis https://www.facebook.com/AntiReservationIndia https://twitter.com/imsumit99 https://twitter.com/68e11543967046d https://twitter.com/atulkumarawasth https://twitter.com/as_465 https://twitter.com/68e11543967046d https://twitter.com/harishmohamed58 https://twitter.com/psjangin https://twitter.com/tiwariumeshlkw https://twitter.com/yogeshjangid592 https://twitter.com/imaakhyan https://twitter.com/nimi2biju https://twitter.com/MukeskK Self-Breeding Fake News 10 The International Journal of Information, Diversity, & Inclusion, 7(1/2), 2023 ISSN 2574-3430, jps.library.utoronto.ca/index.php/ijidi/index DOI: 10.33137/ijidi.v7i1/2.39409 @PBhavit, @RAMESHK43308659, @valaramkalbi981, @Prakash9696P, @Ganesh47224, @laxminarayan793, @ashutosh6485, @sunnytyagi545, @HinduDharma1, @sagenaradramuni, @Bhaskarg77G @atulkumarawasth, @avinashchoubey, @Prakash9696P, @Ganesh47224, @valaramkalbi981 #Hindutva, #hindunationrow, #Nationwantsrammandir, #NaamVaapsi, #RamMandir, #AntiNationals, #TajMahal, #Tejomahalya Bots are types of software that automate interactions across social networking sites. On Twitter, the profiles we tested in column one in Table 4 indicate that fake profiles with no profile picture and followers are intentional with a hidden purpose. The profiles we discovered are fake news sources in political communication. When one politician posts a particular message, it goes to a target audience networked through these kinds of profiles. There are blogs and Facebook pages that talk to a conservative audience. The information disseminated through these blogs needs to be more scientific; instead, they are opinionated and partisan, which gets high treatment, appreciation, and acceptance among a vast audience (see Table 6). Table 6. Blogs and Facebook Pages Speaking to Conservative Audiences Facebook Page Blogging Site 1 2 thesanatanadharmis, AntiReservationIndia indiannationalisticpost.blogspot.in, hinduonline.blogspot.in, varnam.nationalinterest.in, opindia.com, hindujagruti.org Posting and sharing about divisive nationalistic sentiments have more takers; Facebook pages are proof (see column one in Table 6). Hashtags subjected to rigorous digital, ethnographic scrutiny give insight into increasing attempts to discredit nationalist leaders and stalwarts of the freedom movement, and polarizing society along communal lines has become commonplace (see column three in Table 4). After scrutinizing Twitter profiles, it was evident that political opponents presented as inefficient and corrupt were more fashionable (see Table 7). https://jps.library.utoronto.ca/index.php/ijidi/index https://doi.org/10.33137/ijidi.v7i1/2.39409 https://twitter.com/PBhavit https://twitter.com/RAMESHK43308659 https://twitter.com/valaramkalbi981 https://twitter.com/Prakash9696P https://twitter.com/Ganesh47224 https://twitter.com/laxminarayan793 https://twitter.com/ashutosh6485 https://twitter.com/sunnytyagi545 https://twitter.com/atulkumarawasth https://twitter.com/Prakash9696P https://twitter.com/Ganesh47224 https://twitter.com/valaramkalbi981 https://www.facebook.com/thesanatanadharmis https://www.facebook.com/AntiReservationIndia http://indiannationalisticpost.blogspot.in/ http://hinduonline.blogspot.in/ http://varnam.nationalinterest.in/ Self-Breeding Fake News 11 The International Journal of Information, Diversity, & Inclusion, 7(1/2), 2023 ISSN 2574-3430, jps.library.utoronto.ca/index.php/ijidi/index DOI: 10.33137/ijidi.v7i1/2.39409 Table 7. Twitter Profiles Twitter Profiles Hashtags Distorting History 1 2 @PBhavit, @RAMESHK43308659, @valaramkalbi981, @Prakash9696P, @Ganesh47224, @laxminarayan793, @ashutosh6485 #Nationwantsrammandir, #NaamVaapsi, #RamMandir, #AntiNationals, #TajMahal, #Tejomahalya There were scenarios in cases like @Prakash9696P, @Ganesh47224, and @laxminarayan793 in which profiles aggressively shared and promoted caste-related news (see column one in Table 5). We found these profiles by closely observing the personal websites of politicians and political parties (see Table 3). These websites were found to be single-handed solutions to gather all the social media profiles in one place. Well-organized cyber armies created and shared hashtags (see column two in Table 7). The content goes to the fan following, who share the same content with their extended networks. In this vein, the cyber armies are inscribing their perspectives on the head of the audience. We undertook a keyword search on Facebook with the hashtag #Nationwantsrammandir, leading us to a whopping list of profiles, public Facebook forums, and discussions about Ram Mandir, a disputed site in India that is sacred to both Hindus and Muslims. Some profiles tagged with the hashtag above are listed in Table 8. Table 8. Profiles Tagged with #Nationwantsrammandir Pages Followers Personal Profiles Followers @Resurgingdharma 32826 Shivangi Kharwal (facebook.com/shivangiABVP1) 17157 @pratapgarhdevelopment 223862 R J Kiran (facebook.com/Kiranmai.RJ/) 80543 @bhagwanshriganesh 14720 Niladri Bose (facebook.com/NiladriBose2015) 5232 @swayamsevaks 23944 Vishal Sharma (facebook.com/vishalsahotas) 1270 These fan pages and profiles aggressively follow Hindutva right-wing ideologies and disseminate information. However, the authenticity of the shared news is not their priority; instead, strong faith in the leader, ideology, and right-wing causes defined their activities (see Table 8). https://jps.library.utoronto.ca/index.php/ijidi/index https://doi.org/10.33137/ijidi.v7i1/2.39409 https://twitter.com/PBhavit https://twitter.com/RAMESHK43308659 https://twitter.com/valaramkalbi981 https://twitter.com/Prakash9696P https://twitter.com/Ganesh47224 https://twitter.com/laxminarayan793 https://twitter.com/ashutosh6485 https://twitter.com/Prakash9696P https://twitter.com/Ganesh47224 https://twitter.com/laxminarayan793 Self-Breeding Fake News 12 The International Journal of Information, Diversity, & Inclusion, 7(1/2), 2023 ISSN 2574-3430, jps.library.utoronto.ca/index.php/ijidi/index DOI: 10.33137/ijidi.v7i1/2.39409 Consider the profile description of a Twitter user we have studied in the digital ethnography, @avinashchoubey, on 12 June 2014. The description showed that “IT professional. Honorable PM Shri Modiji follows me. Blocked by Anti Modi Folks- Sushma, Rajdeep, Sagarika, Farhan, Mahindra, Javed Akhtar, etc.” (Avinash Choubey, n.d.). This profile is an example of partisan political communication. The user describes that the anti-Modi faction blocked them, so they may not see competing perspectives online. Our survey on Twitter profiles like @HinduDharma1, @sagenaradramuni, and @Bhaskarg77G has found that aggressive partisan information sharing is most sought after (see column one in Table 4). Twitter activities, Facebook posts, and streaming videos appear in parochial and subjective political culture. Caste splinter groups, communal factions, lobby blocs, militants, and hate mongers take up their partisan dialogue colonies. Results This paper investigated how automation of fake news through social bots operates in the conflict zones in India and validates the second proposition stated in our hypotheses. The following propositions are derived from the data. A 2017 estimate suggested that 23 million bots operate on Twitter, whereas 140 million bots are on Facebook and 27 million bots are on Instagram (de Lima Salge & Berente, 2017). Humans need not tell bots what to do. They simulate the behaviour of human beings in a social media environment. They interact with other users and share information and messages (Morstatter et al., 2016). On social media, bots are capable of several social interactions. These make them appear to be regular human-like people. They respond to postings. They respond to questions from other users. They can search for influential users on a particular social media platform. For instance, bots look for Twitter and Facebook users with many followers. The survey found fake profiles on the profiles listed in the data set (see Table 6). The majority of them were created recently, and the same pattern can be found in the social media accounts of the politicians surveyed. Notice that if Rahul Gandhi, a bot, has many followers, it can send that news to the followers’ news feeds that might interest them. It can even send false information to the news feeds that the followers may like. That also means bots need not verify the news shared to the timelines or news feeds. Bots can contact influential social media platforms by sending them questions to get noticed and generate trust. When other Twitter and Facebook users see the exchanges taking place, the same bots also gather trust. Bots generate debate by posting messages about trending topics (Ferrara et al., 2016). Due to the AI capabilities attached, social bots can spread fake news. They can search and retrieve information that has not been validated on social media. On social media sites, bots post continuously to newsfeeds. They spread unverified information using trending topics and hashtags (see column three in Table 4). These are the main strategies to reach a broader audience; they can often perpetuate fake news in terrifying proportions. This study found that social bots spread fake news in two possible ways. First, bots keep telling fake news that is trending on the Internet; however, they never testify to the facts of the news. Bots provide users with that information because it might interest them. Since bots know certain news that one might like, they want to let them know. Second, bots use the same pieces of false information to reply to the postings of real social media users. They use false news to respond to comments and postings. The algorithms upon which bots function assume they are the correct information because the news is trending as per the assumption of their algorithm. That also https://jps.library.utoronto.ca/index.php/ijidi/index https://doi.org/10.33137/ijidi.v7i1/2.39409 Self-Breeding Fake News 13 The International Journal of Information, Diversity, & Inclusion, 7(1/2), 2023 ISSN 2574-3430, jps.library.utoronto.ca/index.php/ijidi/index DOI: 10.33137/ijidi.v7i1/2.39409 means bots cannot distinguish between what is false or true as a human brain can do. After all, falsehood is a subjective issue. The tactics bots use can work because average social media users believe what they see being shared by others on Facebook and Twitter and through trending hashtags. Here, a user is expected to scrutinize the veracity of user-profiles and the information sources that disseminate information more closely (Extremist Content and Russian Disinformation Online, 2017). Bots could be more efficient at deciding what is original and false. They could be smarter. However, humans are intelligent, albeit emotional. Thus, it is worth factoring emotions into the role of real people in spreading fake news. Bots may exist even in the future. However, most of the time, the spread of fake news results from human behaviours. Artificial Intelligence and People's Sensitivity Bots learn how people use social media sites, how often they visit a particular website, and which pattern of information people like the most. How bots spread fake news depends on how people behave on social media (Vosoughi et al., 2018). Therefore, it is the behaviour that bots learn which is the critical factor pushing fake news. Human behaviour on social media sites approves a particular message to go into other users' news feeds. If a Facebook post receives more likes on social media irrespective of the content, people are more likely to form their viewpoints upon seeing the content. The more likes people give to video content on YouTube, the more YouTube videos of the same nature people will see (Walther & Jang, 2012). The more agreement on a blog post discussion, the more likely it is to influence one's perspective (see Table 5). Hashtags fill news feeds with the same content one wants to see while preventing the same users from receiving opposing or alternative views or news. A user’s comment can influence others’ thoughts about the topic. This analogy works in the fake news environment. The more people see and agree with a fake piece of news; the more one would consider it true (Walther et al., 2018). Popularity indicators tend to disseminate more fake news among ordinary people. Posts with more likes, comments, and shares attract more attention from the online audience. Therefore, these popularity indexes are more likely to will attract more people to news and get more likes, shares, and comments (Tandoc et al., 2018). People are likelier to share false than accurate information (Vosoughi et al., 2018). Humans like novelty, and they like to share it with others. False information is always novel. This is evident in columns one and two in Table 5, illustrating how fake profiles operate and filter false information to a broader audience. The information conveyed through "clickbait" and rumours have novelty. It gets people's attention, and they spread it to as many other people as possible. Emotions profoundly affect the dissemination of fake news on social media. This is evident in column two in Table 7, which illustrates how hashtags that contain distorted historical information have a huge fan following and viewership. Seeing genuine news produces mixed feelings. Seeing fake news produces feelings of anxiety and shock. These emotions and feelings are essential when sharing something on social media. Political news is one of many areas that draws emotions from social media users. Fake news related to caste, science, ghost stories, minorities, inter-faith marriage, coronavirus, and love jihad finds an audience (Lohr, 2018). https://jps.library.utoronto.ca/index.php/ijidi/index https://doi.org/10.33137/ijidi.v7i1/2.39409 Self-Breeding Fake News 14 The International Journal of Information, Diversity, & Inclusion, 7(1/2), 2023 ISSN 2574-3430, jps.library.utoronto.ca/index.php/ijidi/index DOI: 10.33137/ijidi.v7i1/2.39409 Analytics and Cookies Social media analytics helps political actors sustain fake news. Websites, Facebook pages, and blogs that contain false information target their audience (see Table 6). Analytics enable them to ensure that what they send is what people are more than likely looking for (Emplifi, n.d.). Analytics gives the audience profile; it can then help send messages that convince each individual. Political actors leverage the possibilities offered by cookies to create an audience where their messages get a liking. Cookies on a computer or mobile system track users’ actions on the Internet. They track all the websites one has visited. Cookies, once agreed to be saved, even store cookies from websites that the users have not logged on to, which are called third- party cookies. This creates a problem. These cookies saved from websites someone has never visited report that the browser has visited them. That also means some third parties have collected data about people without permission. These are called tracking cookies, or trackers for short, and they lead to a big question: Who would be so interested in tracking your movements on the Internet? The answer is straightforward. It is everyone. We are living in the age of data capitalism. Those possessing data about you control you. This is a gateway to an attention economy. Trackers are deployed by certain firms that collect and then analyze users’ behaviours on websites to personalize ads. Take the case of Facebook. Many web platforms embed Facebook's like button on their website. Why? Facebook has a data-sharing arrangement with other websites. Both share details about your habits, attitudes, preferences, interests, and dislikes through this data-sharing arrangement. Every time you visit a website, it reports to Facebook that you have visited them. This happens even if you do not press the like button on your visited website. It also happens whether or not you log into Facebook. Whether you are a Facebook user or have a Facebook account does not matter. What is the advantage for the owners of the website? The particular website can have a wider audience. The website can also look at reports from Facebook about who visited its website (Nield, 2017). Furthermore, it can arrange deals with Facebook to show user newsfeed ads. Facebook, Google, and other prominent data platforms provide trackers to analyze the websites users have visited. They profile what users do while on websites. They gather information about what other pages were opened, what was clicked on, and how much time was spent there. They can analyze it by running scripts on users’ web browsers. Users may not even be aware of it (Tiku, 2017). The website’s users visit, and their actions on those websites give valuable information about them to social media analytics firms. This information is used to calculate models to predict buying behaviour. The firms that collect these personality profiles sell their products through micro-targeting. They select and deliver the advertisements users will most likely react to positively. Many are often unaware that Facebook sells ads to companies to sell products like the ones users looked at on other websites. Many users are often unaware that Google and other data giants have data-sharing agreements with websites through which they show you ads to sell products. Have you ever looked at shoes on a shoe-selling website, cars on a car-selling website, or clothes on a fashion website and then the next time you were on YouTube, Google, or Facebook, there were ads for shoes, cars, or clothes? That is how the cookie works. Some people do not mind that their data is used to show them ads for products they like and to make these ads look more appealing to them. However, some people object to the practice. One concern is the data’s purpose. Not everyone is in the business of approving cookies and tracking https://jps.library.utoronto.ca/index.php/ijidi/index https://doi.org/10.33137/ijidi.v7i1/2.39409 Self-Breeding Fake News 15 The International Journal of Information, Diversity, & Inclusion, 7(1/2), 2023 ISSN 2574-3430, jps.library.utoronto.ca/index.php/ijidi/index DOI: 10.33137/ijidi.v7i1/2.39409 internet service providers (ISPs) for promoting household or harmless products. Some media analytics firms do not want to sell you a product like a car, a smartphone, or a book. What if some firms analyze your data to convince you to vote for a political candidate? What if a company specializing in analyzing data about you engages in impression management? What if an organization specializes in analyzing data to indoctrinate people ideologically and selectively shares news to your news feeds on Facebook and Twitter? These scenarios are not hypothetical situations. Some firms specialize in "pushing your buttons" to persuade users to vote for one candidate or political party or another based on socio-political issues (Howard, 2020). Some firms specialize in creating perspectives (Woolley & Howard, 2018). These firms can let users see candidates' positions on issues that algorithms in these firms believe are appealing to them. These firms can make users see fake news stories using the web or social media. These firms can selectively share the news that reinforces users' views. This corporate practice is a dangerous blend of technology and ideology. Once users respond to these stories or comment on them, the data about their preferences become even more refined. The media platforms do not decide what to show users. However, they get the profile of users’ preferences and associations. They sell access to everyday citizens to advertisers, and their messages automatically come to consumers using the social media platform (Albright, 2016). Micro-targeting uses data to show specific messages to those likely to read, like, and share such a post and click on the website where it originated. Fake news thrives on these websites, which use cookies to conduct social media analytics. That said, potential actors with a blend of technology and ideology can create an ideological bias in users. The micro-targeting strategy used during the BREXIT campaign in the U.K. in 2016 and the 2016 U.S. presidential election proved this blend of technology and ideological bias. (Grassegger & Krogerus, 2016). Fake news flourished during these events as cookies on web browsers of social media users showed them selective news, which created a favourable public image for political actors. Cambridge Analytica and its inventory of user data have profiled social media users for selectively filtering fake news on the social media feeds of voters. (Cadwalladr & Graham-Harrison, 2018). People have raised concerns that ar micro-targeting affected outcomes in both elections. A lot of the micro-targeted messages were fake news. The point is clear that social media analytics combined with fake news websites will destroy civic discourse. It can micro-target groups and people for political and other advantages and reinforce a particular view as the reigning view among ordinary people (Albright, 2016). It can thus lead to a situation of confirmation bias in which users love to see only those that they love to see. Our brain loves to align with things that conform to our convictions and avoid those that refute them. That means cookies can create a situation called a filter bubble. Micro- targeting works in a situation of intense social polarization. The data in Tables 5, 6 and 7 provide evidence of the role of cookies in sending fake news to a greedy audience. Political actors can send cookies to users’ devices and store information about users’ likes and dislikes, thereby indoctrinating users in their favour. Trolls Trolls perpetuate fake news. It is their primary mechanism to sustain false information. In the context of fake news, they are humans having profiles on social media platforms, mostly fake. Attacking a perceived enemy by any means forms their only purpose in cyberspace. They https://jps.library.utoronto.ca/index.php/ijidi/index https://doi.org/10.33137/ijidi.v7i1/2.39409 Self-Breeding Fake News 16 The International Journal of Information, Diversity, & Inclusion, 7(1/2), 2023 ISSN 2574-3430, jps.library.utoronto.ca/index.php/ijidi/index DOI: 10.33137/ijidi.v7i1/2.39409 generate comments that argue with people. Trolls insult and name-call their opponents. Targeting public figures to undermine their public visibility, trolls can take drastic measures if they do not like an idea that one holds. Constantly harassing people who align with an opposite ideology, trolls negate the spirit of democracy. Trolls have no sense of tolerance. They intimidate individuals who post ideas that those behind troll accounts do not like. The data recorded in Tables 5, 6, and 7 tracks the vile nature of trolling on the frontlines of conflict zones in India. Political trolls operate through social divides. They support and promote fake news stories that they ideologically align with. Trolls are often vicious in their comments and get other regular users to be wicked, too, making cruelty and disinformation a normal part of the online experience. That is how trolls turn fake news into truth for ordinary social media users. The Russian Internet Research Agency has been supporting trolls for many years. It allegedly produced fake news to manipulate the U.S. election in 2016. People behind trolls have a well- planned cyber troop. They have agents. They created social media accounts long before the U.S. campaign started. It made their social media profiles look like typical Americans. They chose American names. They used photos and descriptions of Americans and used their fake family details online. They “liked” things and joined discussion groups. They issued innocuous messages. They made friends and acquired followers in the American social media space, thus gaining trust among a section of voters. Finally, they infiltrated the American electoral scenario until the election. Then they exposed the real nastiness. They supported fake news. It fostered disbelief in real news and distracted voters from facts. (Reuters Staff, 2019). This was well documented in the Congressional investigations and indictments handed down by Robert Mueller’s investigation into possible 2016 election improprieties by Russia (United States of America v. Internet Research Agency et al., 2018). A summary of Russia’s Social Media Influence Operations is available in the text of Clint Watts’s testimony before the Senate Judiciary Committee, Subcommittee on Crime and Terrorism (Extremist Content and Russian Disinformation Online, 2017). This research found home-grown varieties of trolls in India (see Tables 5, 6, and 7). Social media campaigns in the general election of 2014 and 2019 are testimonials of this. Fake accounts (see Table 5), targeted attacks (see Table 7), micro-targeting, fake discussions, defaming opponents, and trolls have significantly damaged civic discourse in India. However, less is known about them since what they do is not illegal, and they have not been formally investigated. As per laws in India, one cannot bring discussions and activities akin to hate speech on social media to the attention of law enforcement agencies. Therein lies the problem. Words used by people on social media and the ideas and emotions they represent are subjective facts. They cannot be quantified to appear as objective to laws. Conclusion This research had two core propositions in the research hypotheses: fake news finds synergy with conflict zones of India in which some forces are using it to perpetuate divisions such as caste, gender, class, language, and regional differences, and automated social media accounts are spreading false information that adds to social polarization in India. Tables 1, 2, and 3 represented the empirical sources of this proposition, where we found that Facebook, Twitter, websites, and online platforms serve the campaigning goals of political parties and politicians. We validated that fake news, bots, AI, and cookies find synergy and operate to destroy democracy with dangerous implications. https://jps.library.utoronto.ca/index.php/ijidi/index https://doi.org/10.33137/ijidi.v7i1/2.39409 Self-Breeding Fake News 17 The International Journal of Information, Diversity, & Inclusion, 7(1/2), 2023 ISSN 2574-3430, jps.library.utoronto.ca/index.php/ijidi/index DOI: 10.33137/ijidi.v7i1/2.39409 To answer the study’s second research hypothesis, how social media bots automate fake news in conflict zones, we found that several hashtags that form the constituency of conservative politics often gain visibility. We believe the number of fake profiles identified in this study is an essential indicator of automated accounts' presence and possible influence on social media. Interestingly, many inactive social media accounts initially identified in the dataset that sent several tweets and timeline posts did not yield a bot score. The qualitative examination of one of these accounts, which sent several tweets, shows praise for right-wing politicians and spreads unverified information about the god-like image of Prime Minister Narendra Modi alongside tall claims that he can fix India’s problems. To understand who these bots primarily reference and mention, we found that they mostly cite content derived from social media, not mainstream media outlets. The hashtags are tagged by measuring their trending status in cyberspace. These bots are followed by personal accounts, citizen journalists, grassroots organizations, and anonymous accounts. The prominence of citing, retweeting, and mentioning social media content shows that the bots amplify news on India’s social divides, thus perpetuating polarization. This research identified hashtags, Facebook posts, Tweets, and social media accounts that synergized with fake news and conservative politics. The paper verified that fake news and automation of news found synergy with conflict zones in India. Politicians share fake news using the opportunities of social bots, which, though not able to distinguish like the human brain, have the potential to identify what is trending and share the same with a broader audience. Potential stakeholders use their knowledge of the sophistication of social media technology to their advantage and push their agenda with the help of manipulative technology. This research paper uncovered that bots, AI, and cookies are manipulative technologies that help people achieve gains they would not otherwise achieve. The proposition above is derived from several sources of data. Even though some literature on social media bots highlights the controversial and anti-social nature of automated accounts, the findings of this study show that the majority of bots in the mainstay of the digital political communication landscape in India spread the news on caste, religion, and Hindutva. Technology infrastructure in the information-sharing universe on social media is fluid. Therefore, the nebulous character of the information environment makes it more complicated to bring in regulations on each content delivery and reception point. The content requiring more regulation stems not from human actors but is factored by an autonomous delivery system, which this paper identified as self-breeding fake news. Recommendations Extremists benefit from online forums and escape law enforcers by the cover of subjectivity. They use connected technologies to promote their opinionated ideologies. The Internet allows extremists the avenue to access a potentially enormous audience. It facilitates communication among like-minded people across borders and oceans by anonymously and cheaply enhancing their ability to promote and recruit for their extremist causes. Only some of what we speak in cyberspace can fall under the purview of laws. Outside the Internet, our actions may invite legal sanctions, but activities attain a new form on the Internet. The space where we engage is a critical point, to begin with. Currently, no existing laws in India consider cyberspace as a physical https://jps.library.utoronto.ca/index.php/ijidi/index https://doi.org/10.33137/ijidi.v7i1/2.39409 Self-Breeding Fake News 18 The International Journal of Information, Diversity, & Inclusion, 7(1/2), 2023 ISSN 2574-3430, jps.library.utoronto.ca/index.php/ijidi/index DOI: 10.33137/ijidi.v7i1/2.39409 space. All laws connect cyberspace with devices and information-carrying mediums. The prevailing laws, even with the most liberal interpretation, could not deal with many crimes reported in light of cyberspace. We do not have a sound legal system to manage fake news in India, which also deals with automated fake news ecosystems. Future research is required to investigate automated self-breeding of fake news under the radar of law enforcement agencies. Endnotes 1 We have reached the conclusion that profiles are inactive tracking two factors; whether profiles are sharing content, and if they engage with other social media profiles over a period of time. Those profiles which test negative are labeled inactive profiles. 2 Mission272+ is the name of the campaign that was steered by the Bharatiya Janata Party and the alliance, the National Democratic Alliance (NDA) it led to win the general election in 2014. References Adhikari, G. (2011). The intolerant Indian: Why we must rediscover a liberal space. Harper Collins India. Albright, J. 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Computational propaganda: Political parties, politicians, and political manipulation on social media. Oxford University Press. Author Bios Dr. P. R. Biju (bijugayu@gmail.com) is a writer and teacher specializing in digital technology, artificial intelligence, algorithms, and their impact on society. He is the author of Political Internet (2016), Intimate Speakers (2017), Selfie Sex (2018), and Slave Technology (2019). His upcoming book is about the impacts of artificial intelligence on society. Dr. Biju currently works at Government Brennen College, Thalassery, India. Dr. O. Gayathri. (gayubijuspeak@gmail.com) is a researcher and teacher specializing in women, technology, and movement. Dr. Gayathri currently works at Government College Madappally, Vadakara, India. https://jps.library.utoronto.ca/index.php/ijidi/index https://doi.org/10.33137/ijidi.v7i1/2.39409 https://firstdraftnews.org/articles/fake-news-complicated/ https://digitalcommons.law.uga.edu/cgi/viewcontent.cgi?article=1064&%20context=cle https://digitalcommons.law.uga.edu/cgi/viewcontent.cgi?article=1064&%20context=cle https://doi.org/10.1007/s40979-019-0049-x https://www.technologyreview.com/2020/01/08/130983/%20were-fighting-fake-news-ai-bots-by-using-more-ai-thats-a-mistake/ https://www.technologyreview.com/2020/01/08/130983/%20were-fighting-fake-news-ai-bots-by-using-more-ai-thats-a-mistake/