IMPACT OF BIG DATA ON MEDIA CONTENT REGULATION

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This article is written by Anish of BALLB Hon’s of 3rd Year of PUSSGRC Hoshiapur (Panjab University), an intern under Legal Vidhiya

Table of Contents

ABSTRACT

Big Data is changing through the changes in society; it means how society monitors and regulates media content. Previously, the amount of media user-generated data all but made it impossible for regulators to detect disinformation, hate speech, or biased reporting. Additionally, algorithms can now study consumption and engagement to recognize patterns that can be linked to policies. This presents an exciting solution and is ameliorating the gap between content monitoring and regulators, but it also raises important questions specifically concerning surveillance, data privacy, and algorithmic bias. While Big Data is contributing a new tool that ensures fairness and accountability in the governance of new forms of digital media platforms.

KEYWORDS

Big Data, Digital media platforms, Content monitoring, Disinformation, Hate speech, Consumption patterns, Algorithms, Fairness, Accountability, Surveillance

INTRODUCTION

Big Data is reshaping how media content is produced, disseminated, and monitored. The amount of data being generated by social media, websites, and apps is staggering—organizations and governments have tools to better understand the behavior of audiences and to take action on harmful or misleading content, including phony news, hate speech, breaches of privacy, etc., more quickly than before, at least in theory. In addition to these opportunities, Big Data raises serious concerns about censorship, the biases in algorithms, and surveillance. Striking a balance between communicating the public interest and allowing for free expression is complicated further than ever by Big Data and its ubiquitous presence and promise at a time when regulators are moving quickly towards changes in media content regulation and approaches towards regulation of the digital space. Media content regulation must adapt to reflect the fast-moving digital media environment, for example, using data analytics on the big data information available to help regulators act within and uphold democratic expectations and values.

THE EVOLVING NATURE OF MEDIA IN THE AGE OF BIG DATA

Media has transformed in many ways in an ever-digital world today, especially with the use of Big Data. In the past, people got their news and entertainment from newspapers, radio, and television. The most relevant and meaningful news and entertainment were available on the big screen provided by the television. Today, the majority of that information is presented through social media and other online platforms in which Big Data dominates the space. The Big Data is generated every second or minute that consists of an ever-growing amount of digital information. Online media generates accounts of likes, shares, and comments on each post, which is only part of the Big Data, among others like users’ various search histories and location details. In many ways, media companies partly exist to source and analyze this data. The job of media companies is to examine what people like, which stories go viral, and what their audiences think of their posts. Media companies examine and study Big Data to help them create richer and more lucrative content that people are more likely to engage with or consume.

While this transition brings new challenges, it equally brings new possibilities. Content selection algorithms can trap people in a bubble by only showing what they already agree with. In this case, people interact and only see opinions or perspectives as their own. Digital communications have the potential to virally disseminate hoaxes or use untruths and malicious content that can seduce audiences to react before thinking. It becomes a challenge over what to monitor and how regulations or government or monitoring efforts can even begin to approach media consumption at the speed we see today. Big Data also leads to ethical issues such as violations of privacy, hidden surveillance, and controlling what people see or believe.

Nevertheless, Big Data has increased media’s ability to be interactive, faster, and even more able to satisfy people’s particular needs. The challenge is to find the balance between innovation and responsible regulation. With transparency and fairness as keys to successful media, we can continue to cater to society’s needs, and in projecting societal needs, strengthen and allow for more democratic spaces in the context of digital media.

ROLE OF BIG DATA IN SHAPING MEDIA CONTENT

Big Data makes a significant impact on understanding what media content people will view and ultimately share today. Every click, like, or comment on something online brings in data. This data tells media companies what the public is interested in, what is trending, and what piques people’s interest in viewing something. Based on this information, media companies then curate media content that is more relevant and personalized to people’s behaviors or interests. This often takes the form of “recommended videos” or “news articles based on your previous interactions.”

Social media platforms also use big data, in the form of algorithmic organization, to curate the content feed that you see when you log in. Social media algorithms determine what posts and news stories can be seen first. So, the content feed that you see first is “more visible and viable” in a world where algorithms moderate content. But the same algorithmic distribution of content also protects certain views and thus limits public discussion by creating filter bubbles.

Big Data helps facilitate user experience and business decisions, but important questions still arise surrounding privacy, fairness, and power. The use of data can be harnessed in ways that can inform opinions, affect elections, and frame how users understand the world around them, all of which makes it important to think deeply about the implications of using and accessing data responsibly.

CHALLENGES FOR MEDIA CONTENT REGULATION IN THE BIG DATA ERA

SCALE AND VOLUME

As digital platforms increasing rapidly, regulators can hardly keep up with the size of the content and number of ‘actors’. Smaller agencies, particularly in lower- and middle-income countries, face competing resource demands, limited expertise and fragmented regulatory function.

Example: Singapore’s Health Sciences Authority used a confidence-based tiered approach in order to improve the time it takes to review regulatory applications and employ human resources strategically.

VELOCITY OF INFORMATION

Information spreads faster than regulators react. In an emergency situation, misinformation can spread rapidly and become viral before responding through an official source. In today’s world, there is much information that is fake, and that makes management and public safety difficult.

Example: Real-time big data analytics during the Boston Marathon bombing permitted law enforcement to identify patterns and put a response in place quickly.

VERACITY AND MISINFORMATION

The increase in the fake news, generated by AI content has become a big issue of misinformation. Social media platforms face continued challenges in separating the lines between satire, propaganda, and intentional disinformation – particularly during incidences of crises or during elections.

Example: In the case after the Air India Flight 171 accident, misperceptions spread by AI-assisted fakes fooled many sceptics, including some aviation industry professionals.

DATA OPACITY AND ALGORITHMIC BIAS

Algorithms typically function as “black boxes,” meaning that they have a decision making, capacity that operates without transparency. Bias can also lead to discriminatory consequences that can affect decisions in hiring, credit scoring and content moderation.

Example: Amazon ran into issues when its recruitment algorithm downgrades female candidates as a part of its biased training data.

JURISDICTIONAL COMPLEXITIES

Digital content is borderless, but laws with borders mean limited to countries. When laws differ across borders, it leaves cracks in the system. There are gaps in the system that were used by the offenders to escape punishment, shifting to places where rules are weaker and enforcement rarely follows.

Example: Scammers will shut down their operations for some time and find new locations or countries where there is no extradition treaty in order to avoid consequences.

PRIVACY CONCERNS

Personalization is closely tied to data collection and monitoring but if there’s too much data collection or observation of someone, this violates their privacy rights. Overall, it is becoming more challenging to protect consumer data while also providing a good user experience.

Example: Laws such as GDPR are now applicable, in that we must be transparent and have user permission in how we collect and use personal data.

HARMFUL CONTENT IDENTIFICATION

Human moderators can experience negative psychological impacts while AI systems grapple with nuance, context, cultural sensitivity, etc. Neither approach sufficiently detects and removes harmful content at scale.

Example: AI often detects identifying information as a violation, but misses hate speech hidden in the context of satire, while human moderators face emotional demands leading to burnout.

OPPORTUNITIES AND TOOLS FOR BIG DATA ENABLED MEDIA REGULATION

PROACTIVE MONITORING AND DETECTION

Simply put, you can use AI and new technology to continually monitor and analyze social content using intelligent tools, and in the event of an issue such as disinformation, hate speech, or threatening posts, the platform can intervene before it goes bad. If you want a simile to think about, reflect on your smoke alarm in your home. The smoke alarm does not put out fires, but you will get notified early enough that you have an opportunity to react before it grows into your worst nightmare.

Example: If someone posted harmful misinformation using social media, the AI smart tools would be able to recognize specific words or patterns to notify the moderators in real-time. In the case of misinformation, moderators or authorities would be able to act in the moment to remove the content and/or maybe even provide the user community factual information back.

TARGETED INTERVENTIONS

Regulators are now utilizing data to identify where problems are occurring as opposed to administering the rules uniformly. This prioritization is done based on where it matters the most. It’s similar to discovering why someone is sick and treating the actual problem versus giving them medicine regardless of whether they need it or not.

Example: If fake news is propagated in one city, they take action in that area only and not in the whole country. It’s like treating someone for a specific ailment as opposed to treating all of the patients identically.

TRANSPARENCY AND ACCOUNTABILITY

Different online platforms all engage algorithms to determine the posts, videos, or news items you find. Algorithms are filters based on what you click, like, and, in some cases, even what you search. Unfortunately, people do not typically understand the algorithms or why some content is weighed higher than others. This is why we must access the filters of their systems, searching for accuracy and clarity.

Example: If a news app provides users with stories that always show the political stories that come from only one side, the user should know what leads to that outcome. If users can know who built these systems and how systems engage in a decision, it can create trust and online experiences that are fairer and safer.

DATA DRIVEN ENFORCEMENT

In the past, decisions about online content could often have been made on assumptions or high-level principles without actual data. Now, authorities are increasingly able to make more intelligent actions based on real data. They can objectively analyze what people are posting, sharing, or reacting to, and assess issues empirically based on patterns and facts.

Example: If a post is disseminating dangerous health misinformation and it is spreading and drawing attention, data tools can detect that and notify a moderator. Instead of engaging in random censorship of posts, they can focus attention on the ones causing harm. These actions are targeted, fair, and based more on evidence than on estimation; they help make digital spaces more trustworthy and safer for users overall.

COLLABORATIVE REGULATION

Today, in our digital world, there is no one sector that can “fix” the problems related to online conduct on its own. Collaboration is vital between government, media, and technology platforms to provide a safe online environment and uphold the rights of digital users.

Example: If there is disinformation circulating about health, the government provides correct information/guidance, media promotes the truth, and tech platforms remove posts containing harmful misinformation. When sectors partner in sharing dialogue and working together, online regulations become more equitable and effective. Collaborative work solves complex problems faster, maintains public trust, and ensures that people can safely share opinions, information, and news online. Each sector enhances leverage and power within regulations.

LEGAL AND ETHICAL CONCERNS

Big Data is the collection of analyzing and interpreting large amounts of data associated with people’s online activities or consumer-generated actions and interactions like clicks, posts, searches, or views. Media companies use big data to understand the types of content users are interested in and help regulate content one can see on the internet. Also, it can help platforms find harmful or misinformative posts faster.

PRIVACY ISSUES

There are many media companies that use personal data for suggestions like what we watch, like, or share to help suggest posts and ads you might like to see. This has some benefits because if the use of your data to suggest specific posts or ads that you enjoy enhances some aspect of your online experience, this is a good outcome. However, as we have seen, using your data poses significant risks. If your data is used without your permission or is taken by hackers, the data can lead to identity theft or be used for political or commercial exploitation using your identity. Most people are not aware of how much they are monitored. There are serious concerns related to privacy if we know there are weak data protection laws and/or weak enforcement of data protection laws. Privacy means respecting that individuals are informed, careful, and accountable.

LACK OF CLEAR RULES

There are many areas where personal data protection laws are inadequate or ambiguous. What this means is that companies can collect and use your data without limits or oversight. If the company misuses your data.

Example: In 2018, we learned that Cambridge Analytica had secretly collected personal information from up to hundreds of millions of Facebook users, without their informed consent, using this information to target designated voters with political ads in elections including the 2016 U.S. presidential race. Many Facebook users did not even know their profiles were being mined or how their preferences were engineered. This raised significant legal questions related to the privacy of personal data, and it revealed how soft laws can allow such infringements. It also initiated ethical global debate about how Big Data can skew the availability of media content and distort the democratic process.

CONSENT AND CONTROL

There are many users who don’t know how apps and websites collect and use their personal information without their consent. Ethical data is when people are clear on what they are collecting and why. It also includes seeking permission to collect data and allowing users to stop sharing or delete the data whenever they want.

FREEDOM OF SPEECH VS REGULATIONS

Big Data systems help reduce harmful content, such as hate-speech or misinformation. However, it is not uncommon for those filters to be overly-comprehensive and squash opinions, artistic discourse, or news. Such a diminishment of either the right to free speech or contradictory opinions, thus deterring heterogeneous opinions. In order to appropriately gauge interaction within Big Data systems there needs to be fair controlled regulations to the safety of expression and promotion of miscellaneous beliefs.

BIAS AND MANIPULATION

Social media platforms have set of rules and guidelines to determine the content that users are presented with. At times, they promote one viewpoint while suppressing differing viewpoints. This process creates biased sources of news and hinders balanced information. When individuals can only view one side, they become unfound by equity and ultimately hinder their ability to make informed, independent choices as part of a democratic society.

CONCLUSION

The situation surrounding Big Data with respect to regulating media content defines and also raises challenges and opportunities. While Big Data provides some good abilities for real-time accountabilities, focused, and data-driven enforcement to more accurately identify and mitigate disinformation, hate speech and harmful content, issues including Biased on machine learning, Regionalism, and the breach of data privacy leads to legitimate legal and ethical questions. In this case, regulation must account for the precarious balancing of free expression and proper oversight. Regulation happens responsibly where transparency, participatory approaches to algorithmic oversight, and data ethics that prioritizes user values and democratic principles co-exist in the context of Big Data. As media platforms become data-savvy, solutions for preserving trust, public interest and digital rights become equally sophisticated. Media regulation in the Big Data environment will involve inclusive, innovative and accountable new measures in balancing equity, public safety and welfare, as the global community continues to pursue and practice connection.

REFERENCES

<https://books.google.co.in/books/about/Regulating_Platforms.html?id=fI1SEAAAQBAJ&redir_esc=y>

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