{"id":654,"date":"2026-05-12T05:07:22","date_gmt":"2026-05-12T05:07:22","guid":{"rendered":"https:\/\/listenlabs.ai\/articles\/ai-moderated-tests-large-panels\/"},"modified":"2026-07-22T05:20:44","modified_gmt":"2026-07-22T05:20:44","slug":"ai-moderated-tests-large-panels","status":"publish","type":"post","link":"https:\/\/listenlabs.com\/articles\/ai-moderated-tests-large-panels\/","title":{"rendered":"AI-Moderated Interviews vs Traditional Methods at Scale"},"content":{"rendered":"<p><em>Written by: Anish Rao, Head of Growth, Listen Labs | Last updated: July 21, 2026<\/em><\/p>\n<h2 id=\"key-takeaways\">Key Takeaways<\/h2>\n<ul>\n<li>Traditional human-moderated qualitative research moves slowly, costs a lot, and reaches small samples, which creates backlogs that only shrink with more headcount.<\/li>\n<li>AI-moderated interviews run hundreds of parallel, consistent conversations and often match or exceed human insight depth for most enterprise needs.<\/li>\n<li>Listen Labs\u2019 three-layer Quality Guard system combines behavioral screening, real-time AI checks, and human review to protect data quality at 100+ participant scale.<\/li>\n<li>Multimodal emotional intelligence analysis reads tone, micro-expressions, and word choice with timestamp-level traceability across 50+ languages.<\/li>\n<li>Listen Labs compresses the full research cycle to under 24 hours for concept testing, churn analysis, and multi-market work; <a href=\"https:\/\/listenlabs.ai\/book-my-demo\" target=\"_blank\"><strong>book a demo<\/strong><\/a> to see how the platform scales your qualitative research.<\/li>\n<\/ul>\n<h2>Nine Criteria for Comparing AI and Human Moderation<\/h2>\n<p>A clear comparison between AI-moderated and human-moderated qualitative methods starts with shared criteria. The nine dimensions most relevant to enterprise insights teams are:<\/p>\n<ol>\n<li><strong>Research speed<\/strong>, or time from study launch to deliverable<\/li>\n<li><strong>Insight depth<\/strong>, or levels of adaptive probing per topic<\/li>\n<li><strong>Sample quality and fraud prevention<\/strong>, or verification layers and participant controls<\/li>\n<li><strong>Global and language reach<\/strong>, or countries and languages supported<\/li>\n<li><strong>Methodological flexibility<\/strong>, or study types and stimuli supported<\/li>\n<li><strong>Analysis effort<\/strong>, or automation versus manual synthesis<\/li>\n<li><strong>Deliverable transparency<\/strong>, or traceability of findings to source data<\/li>\n<li><strong>Security and compliance<\/strong>, or certifications and data handling<\/li>\n<li><strong>Operational burden<\/strong>, or vendor coordination and internal resource requirements<\/li>\n<\/ol>\n<p>Each criterion appears in the sections below, with figures cited to their source when platforms differ.<\/p>\n<h2>How AI Moderation Scales Depth Across Hundreds of Interviews<\/h2>\n<p>Human moderation runs sequentially, with one moderator handling one interview at a time and averaging 4\u20136 interviews per day before fatigue reduces probe quality. A 200-interview study then requires either a large moderator team, which introduces inter-moderator variance, or a multi-month timeline.<\/p>\n<p>AI moderation runs in parallel. A single AI moderator conducts hundreds of one-on-one conversations at once, applying identical 5\u20137 levels of emotional laddering to every participant without fatigue. Because the probing logic stays consistent across sessions, cross-segment comparisons are cleaner than studies run by multiple human moderators with different styles.<\/p>\n<p>Listen Labs\u2019 AI Research Agent conducts adaptive video interviews that dig deeper on short or interesting answers, similar to a trained human interviewer, but across hundreds of sessions at once. <a href=\"https:\/\/listenlabs.ai\/blog\/what-is-qual-at-scale\" target=\"_blank\">With qual-at-scale, the old trade-off between depth and scale no longer blocks large qualitative programs.<\/a> Anthropic used this capability to complete <a href=\"https:\/\/www.forbes.com\/sites\/iainmartin\/2026\/01\/14\/this-500-million-ai-startup-runs-customer-interviews-for-microsoft-and-sweetgreen\" target=\"_blank\">300+ user interviews in 48 hours<\/a>, surfacing churn drivers 5x faster than their previous process.<\/p>\n<p>Human moderation still excels in exploratory work where themes are unknown, in studies involving trauma or clinical vulnerability, and in elite C-suite interviews where moderator expertise shapes which follow-ups matter. <a href=\"https:\/\/crresearch.com\/blog\/the-moderator-in-the-machine-why-ai-cant-replace-the-human-heart-of-qualitative-research\" target=\"_blank\" rel=\"noindex nofollow\">When the research question is complex, the audience is nuanced, or the decision is highly consequential, human interpretation adds unique value.<\/a> For most enterprise objectives such as concept testing, churn analysis, journey mapping, brand perception, and product feedback, AI moderation delivers consistent depth at far greater scale.<\/p>\n<p><a href=\"https:\/\/listenlabs.ai\/book-my-demo\" target=\"_blank\"><strong>See a live demo<\/strong><\/a> of how Listen Labs\u2019 AI Research Agent runs adaptive interviews at scale.<\/p>\n<h2>Maintaining Quality and Preventing Fraud with 100+ Participants<\/h2>\n<p>While AI moderation enables unprecedented scale, that same scale raises the stakes for quality control and fraud prevention. An estimated 30\u201340% of online survey data shows signs of quality compromise, and many raw survey responses contain fraud even before accounting for AI-generated answers. In a 15-participant qualitative study, three fraudulent completions represent 20% of the evidence and can shift themes and synthesis. At 100+ participants, single-layer screening cannot reliably protect the data.<\/p>\n<figure style=\"text-align: center\"><a href=\"https:\/\/listenlabs.ai\/\" target=\"_blank\"><img decoding=\"async\" src=\"https:\/\/cdn.aigrowthmarketer.co\/1773098685817-eaceb6089d9a.png\" alt=\"Listen Labs finds participants and helps build screener questions\" style=\"max-height: 500px\" loading=\"lazy\"><\/a><figcaption><em>Listen Labs finds participants and helps build screener questions<\/em><\/figcaption><\/figure>\n<p>Listen Labs addresses this risk through Quality Guard, a three-layer system:<\/p>\n<ol>\n<li><strong>Pre-interview behavioral matching<\/strong>. Listen Atlas matches participants on intent and behavioral signals, not just self-reported demographics. Behavioral-level screening uses device fingerprinting, IP analysis, and enrollment pattern detection to flag high-risk participants before any interview begins.<\/li>\n<li><strong>Real-time AI monitoring during interviews<\/strong>. Quality Guard analyzes video, voice, content, and device signals at once. AI moderation detects fraud patterns that human moderators cannot by tracking response latency, linguistic consistency, reasoning depth, and cross-reference validation across every interview. When responses look scripted or shallow, the AI adds depth probes that require real experience to answer convincingly.<\/li>\n<li><strong>Human review and frequency limits<\/strong>. A dedicated recruitment operations team adds manual review. Participants can join at most three studies per month, which removes professional survey-takers. Listen Labs partners only with high-quality, non-commodity panel sources.<\/li>\n<\/ol>\n<p>Commodity platforms and single-layer screening leave room for professional respondents whose polished but fabricated narratives pass basic checks and create false patterns. A three-layer architecture now represents the minimum standard for enterprise-grade qualitative research at scale.<\/p>\n<h2>Reading Emotional Signals That Transcripts Miss<\/h2>\n<p>Emotional signals often decide whether a concept succeeds, yet transcripts only capture what participants say. They miss a pause before a pricing answer, a micro-expression of confusion when viewing a new concept, or a tone shift that signals skepticism behind positive words. Emotional authenticity scoring checks whether expressed emotions match the experiences described, which also flags semantically coherent but emotionally flat responses.<\/p>\n<p>Listen Labs\u2019 <a href=\"https:\/\/listenlabs.ai\/blog\/emotional-intelligence\" target=\"_blank\">Emotional Intelligence feature analyzes tone of voice, word choice, and subconscious micro-expressions<\/a> to surface emotions that transcripts miss. <a href=\"https:\/\/listenlabs.ai\/blog\/emotional-intelligence\" target=\"_blank\">It uses Ekman\u2019s universal emotions framework, the standard in clinical psychology and UX research, tracking anger, anticipation, disgust, fear, joy, sadness, trust, and surprise.<\/a><\/p>\n<p><a href=\"https:\/\/listenlabs.ai\/blog\/emotional-intelligence\" target=\"_blank\">Every emotion is quantified per question and concept, with each label tied to the exact timestamp, verbatim quote, and AI reasoning behind it.<\/a> This traceability separates the approach from black-box sentiment scoring because researchers can see why a given emotion was identified. The feature supports 50+ languages and connects directly with the Research Agent for natural-language queries, charts, and highlight reels.<\/p>\n<p>Human moderators still excel at reading subtle nonverbal cues in live conversation, and <a href=\"https:\/\/crresearch.com\/blog\/the-moderator-in-the-machine-why-ai-cant-replace-the-human-heart-of-qualitative-research\" target=\"_blank\" rel=\"noindex nofollow\">skilled moderators can interpret hesitation, vocal shifts, and body language that transcripts ignore<\/a>. Multimodal AI analysis closes much of this gap at scale, especially for structured work such as creative testing, concept comparison, and usability research where timestamped emotional data is more actionable than field notes.<\/p>\n<h2>Speed and Cost Benchmarks for Large-Panel Studies<\/h2>\n<p>AI moderation changes the economics and timing of qualitative research. Traditional agency programs carry fully loaded costs for moderators, recruitment, facilities, transcription, analysis, and overhead, which makes a 200-interview study unrealistic for many teams. AI-moderated platforms deliver 200\u2013300 conversations in 24 hours compared with 4\u20138 weeks for traditional methods.<\/p>\n<figure style=\"text-align: center\"><a href=\"https:\/\/listenlabs.ai\/\" target=\"_blank\"><img decoding=\"async\" src=\"https:\/\/cdn.aigrowthmarketer.co\/1773098461736-796a7724447a.png\" alt=\"Screenshot of researcher creating a study by simply typing &quot;I want to interview Gen Z on how they use ChatGPT&quot;\" style=\"max-height: 500px\" loading=\"lazy\"><\/a><figcaption><em>Our AI helps you go from idea to implemented discussion guide in seconds.<\/em><\/figcaption><\/figure>\n<p>Listen Labs compresses the full research cycle, including study design, recruitment, moderation, analysis, and deliverables, to less than 24 hours. A Microsoft Director of Data Science summarized the impact: <em>\u201cI can reach out to hundreds of users at one third of the cost.\u201d<\/em> <a href=\"https:\/\/www.forbes.com\/sites\/iainmartin\/2026\/01\/14\/this-500-million-ai-startup-runs-customer-interviews-for-microsoft-and-sweetgreen\" target=\"_blank\">Listen Labs has run over 1 million AI-powered customer interviews for companies including Microsoft, Perplexity, and Sweetgreen.<\/a><\/p>\n<figure style=\"text-align: center\"><a href=\"https:\/\/listenlabs.ai\/\" target=\"_blank\"><img decoding=\"async\" src=\"https:\/\/cdn.aigrowthmarketer.co\/1773098910279-d16bc544a32e.png\" alt=\"Listen Labs auto-generates research reports in under a minute\" style=\"max-height: 500px\" loading=\"lazy\"><\/a><figcaption><em>Listen Labs auto-generates research reports in under a minute<\/em><\/figcaption><\/figure>\n<p><a href=\"https:\/\/listenlabs.ai\/blog\/ai-interviews-beat-focus-groups\" target=\"_blank\">Platforms like Listen Labs add auto-recruiting, transcription, sentiment tagging, and insight summarization<\/a> so teams move from questions to findings in hours. For enterprise teams running 20 or more studies per year, these savings compound into major budget and capacity gains.<\/p>\n<figure style=\"text-align: center\"><a href=\"https:\/\/listenlabs.ai\/\" target=\"_blank\"><img decoding=\"async\" src=\"https:\/\/cdn.aigrowthmarketer.co\/1773099063654-7132de546a42.png\" alt=\"Listen Labs&apos; Research Agent quickly generates consultant-quality PowerPoint slide decks\" style=\"max-height: 500px\" loading=\"lazy\"><\/a><figcaption><em>Listen Labs&#039; Research Agent quickly generates consultant-quality PowerPoint slide decks<\/em><\/figcaption><\/figure>\n<h2>When AI Moderation Becomes the Stronger Choice<\/h2>\n<p>The right moderation approach depends on objectives, timing, audience, and sensitivity. AI moderation becomes the stronger option in the following scenarios:<\/p>\n<ul>\n<li><strong>Enterprise consumer insights teams<\/strong> with growing backlogs that must run more studies without adding headcount, where AI moderation multiplies output for the same team.<\/li>\n<li><strong>UX research groups<\/strong> validating concepts, testing prototypes, or running usability studies with 50\u2013200+ users instead of 5\u201310, where scheduling and no-shows create bias.<\/li>\n<li><strong>Product and marketing teams without dedicated researchers<\/strong>, where AI-assisted study design handles methodology, recruitment, moderation, and analysis.<\/li>\n<li><strong>Agencies and consultancies<\/strong> serving clients on timelines measured in days, needing global reach and niche audiences quickly.<\/li>\n<li><strong>Multi-market studies<\/strong> that require consistent probing across languages and geographies, where human moderator variance and translation overhead increase risk.<\/li>\n<\/ul>\n<p>Human moderation remains preferable for foundational exploratory work with unknown themes, for studies involving trauma or clinical vulnerability, and for high-stakes elite interviews where moderator expertise acts as a research instrument. A hybrid model that uses AI for broad discovery and human moderators for targeted deep dives on surprising findings combines statistical confidence with interpretive depth.<\/p>\n<p><a href=\"https:\/\/listenlabs.ai\/book-my-demo\" target=\"_blank\"><strong>Schedule a consultation<\/strong><\/a> to match your upcoming studies with the right mix of AI and human moderation.<\/p>\n<h2>Managing the Risks of Large-Panel AI-Moderated Research<\/h2>\n<p>AI moderation at scale introduces specific risks that enterprise teams should address before rollout:<\/p>\n<ul>\n<li><strong>Shallow-data risk from poor screener design<\/strong>. <a href=\"https:\/\/kantar.com\/north-america\/inspiration\/agile-market-research\/ai-in-qualitative-research-5-essential-practices-for-quality-at-scale\" target=\"_blank\" rel=\"noindex nofollow\">Nontraditional sample sources often recruit respondents who are not used to reflective, open-ended questions, which lowers response quality regardless of moderation quality.<\/a> Screener design carries more weight in AI-moderated studies because no live moderator can validate credentials in real time.<\/li>\n<li><strong>Hidden recruitment complexity<\/strong>. <a href=\"https:\/\/nexusexpertresearch.co\/blog\/scaling-qualitative-research-with-ai\" target=\"_blank\" rel=\"noindex nofollow\">The main bottleneck in B2B qualitative research often sits in sourcing the right participants, not in moderation technology.<\/a> Platforms without dedicated recruitment infrastructure shift this work back to the research team.<\/li>\n<li><strong>Prompt design errors that scale instantly<\/strong>. <a href=\"https:\/\/questionpro.com\/blog\/ai-moderated-research\" target=\"_blank\" rel=\"noindex nofollow\">A poorly written question in AI-moderated research can derail hundreds of sessions before anyone notices<\/a>, unlike human moderation where a moderator can adjust mid-session.<\/li>\n<li><strong>Reduced flexibility for emergent discovery<\/strong>. AI-moderated interviews trade some flexibility to chase unexpected tangents that might reveal breakthrough insights outside the guide.<\/li>\n<li><strong>Overestimating how much can be automated<\/strong>. <a href=\"https:\/\/kantar.com\/north-america\/inspiration\/agile-market-research\/ai-in-qualitative-research-5-essential-practices-for-quality-at-scale\" target=\"_blank\" rel=\"noindex nofollow\">Without a human quality-check layer, AI-moderated responses at scale can lack the depth needed for action<\/a> even with strong panels.<\/li>\n<\/ul>\n<p>These risks become manageable with the right architecture. Listen Labs addresses them through AI-assisted study design with auto-QA, a 30M verified panel with Quality Guard, and a Research Agent that produces traceable deliverables instead of opaque summaries.<\/p>\n<h2>Decision Framework and Practical Checklist<\/h2>\n<p>Use this checklist to match your research approach to the context:<\/p>\n<ol>\n<li><strong>Timeline under one week?<\/strong> Choose AI moderation for any study with 50 or more interviews.<\/li>\n<li><strong>Sample size above 30?<\/strong> Treat human moderation as a niche option because costs and logistics rise sharply, and use AI as the default.<\/li>\n<li><strong>Topic involves trauma, grief, or clinical vulnerability?<\/strong> Require human moderation for ethical and duty-of-care reasons.<\/li>\n<li><strong>Objective is concept testing, churn analysis, brand perception, or journey mapping?<\/strong> Use AI moderation to reach scale while maintaining depth.<\/li>\n<li><strong>Study is multi-market or multilingual?<\/strong> Favor AI moderation with 100+ language support to remove translation overhead and moderator variance.<\/li>\n<li><strong>Audience is hard-to-reach (below 1% incidence)?<\/strong> Confirm that the platform has dedicated recruitment operations, not only a self-serve panel.<\/li>\n<li><strong>Emotional nuance is the primary data?<\/strong> Require multimodal emotional analysis with timestamp-level traceability, not transcript-only sentiment scores.<\/li>\n<li><strong>Security and compliance are mandatory?<\/strong> Verify SOC 2 Type II, GDPR, ISO 27001, ISO 27701, and ISO 42001 certifications.<\/li>\n<li><strong>Exploratory research with unknown themes?<\/strong> Start with 5\u201310 human-moderated discovery interviews, then scale validated themes through AI moderation.<\/li>\n<\/ol>\n<h2>Frequently Asked Questions<\/h2>\n<h3>How does AI moderation work differently from a human moderator?<\/h3>\n<p>An AI moderator runs one-on-one video interviews using a structured guide with adaptive probing logic. When a participant gives a short or interesting answer, the AI asks follow-up questions that build on prior responses, similar to a trained human interviewer. The main difference lies in consistency and scale, because an AI moderator applies the same probing depth across every participant without fatigue, carryover bias, or scheduling limits. Human moderators often experience quality drops after 3\u20134 sessions per day, with shallower probes and more formulaic follow-ups. AI moderation removes this variance, which matters most when comparing findings across segments, geographies, or time periods. Human moderators still hold an edge in exploratory work, emotionally complex topics, and elite interviews where domain expertise shapes the conversation.<\/p>\n<h3>How does Listen Labs prevent participant fraud at scale?<\/h3>\n<p>Listen Labs uses the three-layer Quality Guard system. First, Listen Atlas matches participants on behavioral and intent signals, not just self-reported demographics, and screens out high-risk profiles using device fingerprinting, IP analysis, and enrollment pattern detection before interviews begin. Second, Quality Guard monitors every interview in real time across video, voice, content, and device signals to detect fraud, low-effort participation, and AI-generated scripts. When responses look shallow or scripted, the AI adds depth probes that require genuine experience. Third, a recruitment operations team adds human review, and participants are limited to three studies per month to remove professional survey-takers. Listen Labs works only with high-quality, non-commodity panel sources, avoiding open-access crowdsourcing panels where self-reported job titles show documented inaccuracy rates of 20\u201330% for B2B recruitment.<\/p>\n<h3>How quickly can Listen Labs deliver results for a large study?<\/h3>\n<p>Listen Labs compresses study design, recruitment, moderation, analysis, and deliverables to less than 24 hours for most projects. The Anthropic case mentioned earlier illustrates this speed, and Microsoft collected global customer video stories for its 50th anniversary within a day. The Research Agent automatically generates consultant-quality slide decks, memos, video highlight reels, statistical charts, and segmentation breakdowns, so teams avoid manual report writing. A traditional agency program with 20 interviews often takes 6\u20138 weeks from design to final deliverable.<\/p>\n<h3>What types of studies can Listen Labs support?<\/h3>\n<p>Listen Labs supports a wide range of qualitative and mixed-method studies, including concept and prototype testing, usability testing with screen sharing, creative testing, brand perception work, consumer journey mapping, multi-market segmentation and localization, ad testing, pricing research, and survey open-end analysis. The platform handles both one-off projects and ongoing continuous research programs. Study designs can include free-flowing in-depth interviews, semi-structured interviews, diary studies, ethnography, and task-based UX testing. Advanced stimuli support covers images, video, audio, PDFs, prototypes, and live URLs, with monadic or sequential randomization, quotas, branching, skip logic, and piping.<\/p>\n<h3>Is Listen Labs compliant with enterprise security and privacy requirements?<\/h3>\n<p>Listen Labs holds SOC 2 Type II, GDPR, ISO 27001, ISO 27701, and ISO 42001 certifications. The platform uses 256-bit encryption, supports enterprise SSO, and never uses customer data for AI model training. These certifications cover information security management, privacy information management, and AI management systems, which align with Fortune 500 procurement standards. For teams with specific data residency or contractual needs, the enterprise sales process includes a pilot phase to validate compliance before full deployment.<\/p>\n<h2>Conclusion: Matching AI Moderation to Your Research Backlog<\/h2>\n<p>The depth-versus-scale trade-off that shaped qualitative research for decades reflects the limits of human moderation, not of qualitative methods. <a href=\"https:\/\/listenlabs.ai\/blog\/what-is-qual-at-scale\" target=\"_blank\">Qual-at-scale fits best when research requires large samples or broad geographic reach, with AI tools engaging hundreds or thousands of participants remotely and asynchronously.<\/a><\/p>\n<p>The criteria that matter most for enterprise teams, including speed, insight depth, fraud prevention, global reach, analysis effort, and compliance, now favor well-architected AI moderation with layered quality controls over traditional human-only studies and fragmented tools. Listen Labs\u2019 30M verified panel, three-layer Quality Guard, multimodal Emotional Intelligence built on Ekman\u2019s framework, and end-to-end Research Agent set a strong benchmark for enterprise qualitative research at scale.<\/p>\n<p>The outcomes highlighted throughout this article, from Anthropic\u2019s rapid churn analysis to Microsoft\u2019s anniversary project and other enterprise deployments, show consistent advantages in speed and scale. <a href=\"https:\/\/listenlabs.ai\/blog\/ai-interviews-beat-focus-groups\" target=\"_blank\">Teams that adopt AI-moderated interviews capture hundreds of candid, one-to-one conversations overnight.<\/a><\/p>\n<p>For teams deciding whether AI moderation fits their next study, a scoped pilot offers a practical starting point. <a href=\"https:\/\/listenlabs.ai\/book-my-demo\" target=\"_blank\"><strong>Connect with Listen Labs<\/strong><\/a> to review your research backlog, audience requirements, and compliance needs, and to see the platform run a live study end to end.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Run AI-moderated interviews across 100+ participants in under 24 hours. Listen Labs delivers qualitative depth at quantitative scale. Book a demo.<\/p>\n","protected":false},"author":52,"featured_media":653,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[1],"tags":[],"class_list":["post-654","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/listenlabs.com\/articles\/wp-json\/wp\/v2\/posts\/654","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/listenlabs.com\/articles\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/listenlabs.com\/articles\/wp-json\/wp\/v2\/types\/post"}],"replies":[{"embeddable":true,"href":"https:\/\/listenlabs.com\/articles\/wp-json\/wp\/v2\/comments?post=654"}],"version-history":[{"count":1,"href":"https:\/\/listenlabs.com\/articles\/wp-json\/wp\/v2\/posts\/654\/revisions"}],"predecessor-version":[{"id":1288,"href":"https:\/\/listenlabs.com\/articles\/wp-json\/wp\/v2\/posts\/654\/revisions\/1288"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/listenlabs.com\/articles\/wp-json\/wp\/v2\/media\/653"}],"wp:attachment":[{"href":"https:\/\/listenlabs.com\/articles\/wp-json\/wp\/v2\/media?parent=654"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/listenlabs.com\/articles\/wp-json\/wp\/v2\/categories?post=654"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/listenlabs.com\/articles\/wp-json\/wp\/v2\/tags?post=654"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}