Social media platforms like Facebook, Twitter, Instagram, etc. have become an integral part of our lives. These platforms generate enormous amounts of data on a daily basis – data about users, their interests, behaviors, social connections, content created and interactions.
This is where data science comes into the picture. Data science techniques help social media companies to derive insights from this data to improve user experience, target advertising, recommend content, and more. In this article, we will discuss the impact of data science on social media.
Understanding User Behavior
Data science can help to understand the user behavior in these ways.
Identifying Usage Patterns
Data science helps identify usage patterns – when users are more active, what content they usually interact with at different times of day, which features they use more often, etc. Social media platforms use this to optimize system performance for anticipated traffic volumes and tweak features for greater adoption.
Sentiment analysis examines social text to determine emotional tone – whether users are expressing positive, negative or neutral sentiments. This provides insights into how users feel about certain topics, features or campaigns on social media. Platforms accordingly improve communication, product features or support system.
Data science builds models to predict which content is likely to get shared rapidly and widely on social networks. These models take into account factors like messaging, imagery, emotions evoked, author’s profile, current trends and more. Platforms use such virality predictions to promote organic reach for branded content.
Personalization & Recommendations
It can be divided into following two main categories.
Customized Content Feeds & Ads
Sophisticated algorithms analyze every bit of user activity – likes, shares, clicks, searches, purchases – to determine their interests and preferences. They then curate and recommend hyper-relevant organic posts, paid ads, suggested profiles and groups to follow. This creates a personalized experience.
Platforms determine similarities across users to categorize them into custom-audiences or lookalike-audiences. They particularly target ads to people predicted to resemble existing users who already have shown a liking for those products – thereby boosting advertising ROI.
Data science provides macro trends about topics being discussed in social communities over time. Network analysis identifies influential members based on how they connect communities. These insights help brands in community outreach programs. Chatbots are also trained on community conversations to automate engagement at scale.
Data science powers social media platforms in many positive ways, however, concerns around user privacy and data ethics have also surfaced. Some are given below.
When platforms show us only information they think aligns with our viewpoints, it creates narrow “filter bubbles” – this risks limiting our worldview and radicalizing opinions instead of enabling informed debates.
Breaches where user data gets exposed due to security vulnerabilities have made people uneasy about how much of their personal information is being tracked for targeting. Stricter cybersecurity measures need to be implemented by platforms.
The very algorithms that optimize engagement can make interfaces “sticky” and habit-forming. The companies may profit from increased time-spent, addiction and mental health disorders resulting from excessive social media use are issues of concern emerging from research.
Spread of Misinformation
Ease of virality also sometimes causes false claims and conspiracy theories to rapidly spread before fact checkers can intervene. Curbing this “infodemic” requires a mix of technology solutions and public awareness.
Balancing Innovation with Ethics
Going forward, social media platforms need to proactively self-regulate and address ethical dilemmas resulting from enhanced data collection and analytics. Governments may also need to update privacy and user protection laws in tune with latest technological capabilities.
In a nutshell, data science elevates social media platforms from just being content hosts to intelligent networks that learn what users want and provide customized, engaging experiences at scale. It powers most of the features and functionalities we take for granted on social media today. The potential of data science in making online social connect even more meaningful is still largely untapped and awaiting exciting innovations.