How modelling social network can help government to curb spread of extremism?

Article by Harsh Kushwah

Social networks have rapidly gained prominence in our daily lives with billions of people sharing their thoughts, opinions, pictures, videos and much more. Overall, it has been an entertaining and thought-provoking phenomenon for people. However, the dark side of social media is often left loosely discussed, often restricted to only negative outcomes about personal image and self-worth.

“Technology alone doesn’t shape Future. Intention of Humans behind it does”

Social media has been vastly employed by extremist and terrorist organizations in order to gain financial and philosophical/idealogical support and followers, recruit terrorists and much more. Popular instance of using social media to spread violent ideologies and recruiting terrorists is the usage of Twitter and YouTube by ISIS. It must be duly noted that over the period of time, especially during the massive surge in terrorist recruitment and online support for the violent ideology of ISIS, Twitter played a crucial role.

There was a mushroom growth of numerous Twitter handles, spreading distorted version of religion, rationalizing violence, appealing the susceptible youth to join their cause and even organizing sudden attacks, especially lone wolf attack, become a cause of concern for governments all over the globe.

So, if nations want to stop and prevent emergence and resurgence of these organizations, they need to study spread of ideologies on social network.

A social media network can easily be described as numerous nodes connected to each other in a fixed or dynamic pattern. These connections are Links, which keeps creating and dissolving with time. Each person in a social media network can be represented as a node in the network. Often they are called agents. Each agent has their own decision making function. Each person has their own decision threshold. State of decision making variable of these agents is dynamic and depends on inputs from external sources as well as their own connections.

Seems confusing? Let’s visualize.

Let’s say you and your friend are part of social media network. If you are a movie enthusiast and have watched a movie, you are more inclined to tell it to your friends. Whether you want them to watch it or avoid it, you will try to influence their decision. Now, your friend who is already viewing trailer of that movie, now gives importance to your review, his decision state for watching that movie will change as per these inputs. As soon as that decision state clears the decision threshold of your friend, he will watch the movie.

Now, imagine this simplistic phenomenon in a social media network with millions of users, connected in different ways.

But how this connects to terrorist organizations and social media?

Well, with growing discontent among particular sections of society, exposed to media reports of violence against them and prevailing injustice against them, a large section of people are now susceptible to these violent ideologies.

However, most of these discontented people have high threshold for adopting a violent means to express their discontent and that’s why we don’t see terrorists and extremists in millions. But among these sections, vulnerable and easily impressed youth which can be persuaded easily, fall in traps to the online influences of these terrorist organizations. Steady feed of online messages, distorted history, fake news, propaganda material (text and graphics) constantly affect the decision state of these individuals, finally pushing them to join these terrorist organizations.

Governments and technology companies face a massive challenge to curb these online extremist influencers, stop these online conversations and prevent emergence of more such social media accounts.

Points to Consider

1. User approved collection of data and post reporting structure feeding into machine learning algorithms, usage of AI and Big Data Analytics can present striking patterns and underlying structure of such networks.

2. Massive data crunching at high speed can help in predicting and preventing extremist violence from happening.

3. Active participation of users of social media platforms to flag and report such violent content and propaganda material.

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