What’s Wrong With Modern Social Listening? Part 1. Query Writing

And What Infegy Is Doing To Fix It

Writing effective queries is one of the biggest challenges in social listening. Most legacy social listening platforms rely on Boolean logic, forcing users to build long, complex keyword strings just to filter the right conversations. An entire ecosystem has emerged to teach analysts the art and science of writing these queries. While this approach works, it’s time-consuming, error-prone, and often limits the insights brands can uncover. At its core, query writing hasn’t fundamentally changed since social listening first emerged in the early 2000s.

At Infegy, we believe social listening should be both powerful and accessible. You shouldn’t need to be a Boolean expert or industry insider to get meaningful insights. That’s why we have been recreating query writing from the ground up with Infegy Starscape. With a smarter visual query builder, a speed-driven architecture, and AI-powered language detection, we’re making it easier to find the insights that matter—without the frustration, time commitment, and complexity.

Let’s review the problems with traditional query writing and how Infegy is fixing them.

Overreliance on Boolean

If you’re new to social listening, you’ll quickly notice that most tools rely on Boolean queries. Boolean logic filters results by specifying what to include or exclude.

At first, it seems simple:

^{Figure 1: Sample Boolean Query}

Figure 1 shows a query that pulls posts mentioning “apple” but excludes “banana.” Easy, right? Not quite. This query misses plurals (apples, bananas) and related variations like hashtags or emojis. To fix that, you’d need a more complex query:

^{Figure 2: Boolean Query with Plurals Which Doesn’t Catch All Possibilities}

Or, you could use wildcards:

^{Figure 3: Wildcard Operators}

Wildcards capture all words starting with apple or banana, but they often pull in irrelevant results.

As queries grow, they quickly become long and difficult to manage. Here’s a real-world example:

^{Figure 4 perfectly displays the four key issues with complex queries like:}

• Hard to Read: Nested parentheses and logical operators make quick parsing difficult.
• Difficult to Edit: Adding or removing clauses risks breaking the query, and pulling the wrong information.
• Error-Prone: One missing parenthesis can completely change the logic, and thus the results.
• Rigid Structure: New product names won’t be captured unless manually updated each time a new product arrives.

We often see Boolean queries that are hundreds or even thousands of words long, making these issues even worse. Boolean works, but it’s far from user-friendly.

That’s why we’re rethinking query writing.

Infegy’s New Query Builder

With the release of Infegy Starscape’s innovative query builder, we’re making query writing more intuitive while preserving Boolean’s power. Our visual, tree-like structure lets users build queries by dragging and dropping logical segments—kind of like Legos.

Infegy Query Builder Video

^{Figure 5: Video of Drag-and-Drop Query Building}

Our query builder is also modular, meaning you can pre-build filter segments—like Not-Safe-For-Work terms or audience demographics—and reuse them as needed. When you’re done, you can save and share queries across your organization, ensuring consistency and eliminating the need to rebuild queries from scratch.

This approach directly improves the four major Boolean pain points:

• Easier to Read: No more parsing through thousands of characters—your logic is laid out visually.
• Easier to Edit: Modify specific nested clauses without losing your place.
• Less Error-Prone: Errors are localized to individual branches of the logic tree, reducing their impact.
• More Flexible: Dynamically add or remove segments as needed.

Boolean queries have been the industry standard for years, but they don’t have to be the only way. With Infegy’s new query builder, social listening is more powerful—and more user-friendly—than ever.

Bias Towards Hyper-Specific Queries Due to Query Limits

Hyper-specific, overly complex Boolean queries—like the one in Figure 6—go hand in hand with the long, complicated queries we discussed earlier.

^{Figure 6: Long, Hyper-Specific Boolean Queries About Tesla’s Cybertruck}

This isn’t their fault—it’s what most legacy social listening tools have forced them to do. Many platforms are slow, clunky, and take hours—or even days—to return results. Running a broad, simple exploratory query often overwhelms the system or gets throttled by limits on mentions. Some platforms even charge by the mention, so a broad search itself could cost hundreds or even thousands of dollars. When query limits are strict—sometimes allowing as few as five at a time—every search has to count. Wasting one on an overly broad query isn’t an option, forcing users into a hyper-targeted approach.

The result? Users rely on highly specific queries to avoid these constraints, but in doing so, they unintentionally exclude large portions of the conversation. This skews key metrics like post volume and sentiment, making it easy to miss critical insights.

Infegy’s Speed-Based Architecture

Infegy Starscape doesn’t have these limitations. We collect our data and have built our platform from the ground up for speed and performance. Additionally, we do not have mention limitations. Whatever the query returns is what you will see. No extra charges. No careful monitoring. That means you can run a broad query for “Cybertruck” and instantly retrieve millions of mentions—like the 2,493,952 documents we pulled in just 1 second.

^{Figure 7: AI Summary of CyberTruck-Related Conversations Over the Last 3 Years}

From that broad dataset, you can dive into our AI Summary (Figure 7) to uncover the key narratives shaping the conversation. Each blue term button in the Summary links to a drill-down query, allowing you to explore specific topics with a single click. As you refine your focus, you can continuously select new narratives, with Infegy dynamically generating the exact query terms you need at each step—enabling an iterative exploration without the guesswork or manual query building process.

^{Figure 8: Automatically Generated Query Using Infegy’s AI}

Instead of building exhaustive queries upfront, you can start with the full conversation and let the data guide your exploration. Infegy transforms social listening from a rigid filtering tool into the most robust research platform, helping you discover insights you didn’t even know to look for.

Limited Adaptability to Evolving Language

Traditional Boolean social listening queries struggle to keep up with how language evolves. Slang, memes, cultural shifts, and even new product names can change rapidly, making it difficult to maintain an effective Boolean query. A term that was popular six months ago might be outdated today, meaning analysts have to constantly update their queries to ensure they’re capturing relevant conversations. Without frequent adjustments, Boolean queries risk missing key insights or, worse, returning misleading data.

Infegy’s NLP and AI-Powered Context Detection

Infegy eliminates the need for constant query maintenance by leveraging Natural Language Processing (NLP) and AI-driven context detection. Instead of relying on rigid keyword lists, Infegy understands the meaning behind conversations, allowing it to recognize evolving terminology, emerging trends, and contextual relevance. This ensures that users capture the full scope of a conversation without having to manually adjust their queries every time language shifts.

Here’s how Infegy’s NLP makes social listening more adaptive and intelligent:

1. Entity Detection

Infegy automatically identifies brands, products, and people—even when they appear as abbreviations, slang, or misspellings. This helps capture relevant mentions without requiring extensive keyword lists.

2. Language-Agnostic Themes

Rather than relying on exact keywords, Infegy detects concepts and themes, such as purchase intent, across multiple languages. This allows for a broader, more accurate understanding of discussions without the need for separate language-specific queries.

3. AI-Assist for Query Writing

Infegy’s AI-Assist simplifies query construction by converting natural language input into a properly formatted query. Instead of manually structuring Boolean logic, users can describe what they’re looking for, and the system generates a structured query in our query builder that aligns with best practices. This reduces errors and makes complex query building more efficient.

Key Takeaways

For years, social listening has relied on Boolean logic, forcing users to build long, rigid queries that are difficult to read, edit, and maintain. This outdated approach limits insights, creates unnecessary complexity, and struggles to keep up with the way language evolves. Many tools also impose technical constraints—slow processing times, mention limits, and cost-based restrictions—that push users toward hyper-specific queries, which can exclude critical parts of the conversation.

At Infegy, we’ve reimagined query writing to make social listening faster, smarter, and more intuitive. With a visual query builder, a speed-driven architecture, and AI-powered language detection, we remove the need for exhaustive Boolean logic while ensuring users capture the full scope of relevant conversations.