People go online for different reasons, and their behaviour can generally be grouped into three categories: active, passive, and negative. Understanding these categories helps us see patterns, make sense of digital interactions, and recognise risks.
Active Motivations: Purpose-Driven Online Use
Active motivations are when someone logs on with a clear goal. This includes checking emails, shopping online for a specific item, paying bills, posting deliberately on social media, or researching a topic.
Active behaviour is observable: time spent is purposeful, interactions are direct, and tasks are completed before the user exits. There’s minimal distraction or deviation from the original goal.
Passive Motivations: Habit, Scroll, and the Hidden Toll
Passive motivations occur when someone goes online without a specific goal, just to see what’s happening. This can include casual scrolling through social media feeds, browsing news headlines, or checking notifications out of habit.
On the surface, passive behaviour seems harmless. It’s repetitive, low-pressure, and often fills time when waiting or avoiding boredom. But when observed closely, certain patterns reveal a negative side. Passive use frequently involves exposure to curated content, algorithms, and repeated scrolling that can actively drain attention and time.
Doomscrolling is a key example. Users scroll through feeds of news, social media posts, or trending topics without intent to act. This behaviour can lead to prolonged exposure to alarming or emotionally charged content. The action itself is measurable: users refresh feeds repeatedly, click links, or consume content continuously for hours without completing a task.
Comparison to curated lifestyles is another observable pattern. Social media feeds often show idealised or heavily edited portrayals of other people’s lives. Users scroll, like, or comment, which signals the algorithm to show more similar content. The measurable outcome is extended engagement: a feed tailored to maximise attention through repetition of appealing or aspirational content.
For example: a person checks Instagram in the morning, sees a friend on holiday, likes the post, then the algorithm serves five more similar posts. They scroll, comparing possessions, appearance, or experiences to their own. The behaviour is passive—there is no active goal—but the effect is cumulative: extended screen time, attention capture, and repeated engagement with content that can indirectly stress or frustrate the user.
Algorithmic reinforcement amplifies this. Each click, like, or comment feeds data back to the platform. The algorithm learns preferences and feeds similar posts, keeping users engaged longer. Observably, this creates a feedback loop: repeated scrolling, repeated engagement, and repeated exposure to similar content, often disconnected from the user’s original intent.
Passive use, therefore, can shift into what some call “negative passive behaviour”: it is not malicious in intent, but it produces measurable effects such as excessive time online, repetitive attention patterns, and engagement with content that doesn’t directly achieve the user’s original goals. Users are effectively nudged by algorithms to stay online and interact with certain types of content.
Negative Motivations: Malicious or Harmful Behaviour
Negative motivations differ because they are externally directed. This includes scammers, fraudsters, trolls, phishing attempts, and bots. These behaviours are observable, deliberate, and measurable: repeated unsolicited messages, links to phishing sites, trolling patterns, or impersonation.
Unlike passive use, these behaviours are intentional and aimed at harming, deceiving, or profiting from others online. Recognising them allows users to protect themselves, block accounts, report content, and reduce exposure to risk.
Understanding Patterns for Better Digital Awareness
By distinguishing active, passive, and negative motivations, users can better navigate online spaces. Active use completes tasks, passive use often fills time but may expose users to algorithmic reinforcement and repeated curated content, and negative motivations represent external threats.
For passive behaviour, awareness of feed patterns, time spent, and the types of content engaged with can help users make measurable adjustments: limiting scrolling sessions, unfollowing repetitive feeds, or using platform tools to reduce algorithmic reinforcement.
