{"id":616,"date":"2026-09-16T05:03:19","date_gmt":"2026-09-16T05:03:19","guid":{"rendered":"https:\/\/www.akarnik.com\/blog\/share-of-voice-citation-monitoring"},"modified":"2026-09-16T05:03:19","modified_gmt":"2026-09-16T05:03:19","slug":"share-of-voice-citation-monitoring","status":"publish","type":"post","link":"https:\/\/www.akarnik.com\/blog\/share-of-voice-citation-monitoring","title":{"rendered":"Share Of Voice Citation Monitoring: Define, Calculate, Track"},"content":{"rendered":"<p><em>Written by: Arjun Karnik, Growth Marketing Specialist<\/em><\/p>\n<h2 id=\"key-takeaways\">Key Takeaways<\/h2>\n<ul>\n<li>Share of voice citation monitoring tracks how often your brand is cited in AI answers across ChatGPT, Google AI Overviews, Perplexity, and Gemini relative to competitors.<\/li>\n<li>The core formula is Share Of Voice = (Your Citations \u00f7 Total Citations Across All Brands) \u00d7 100, with the denominator covering every citation across every tracked brand on every surface.<\/li>\n<li>Four distinct metrics \u2014 citation rate, mention rate, citation share, and citation position \u2014 each point to different fixes when they move in the wrong direction.<\/li>\n<li>Content freshness, fan-out query alignment, and extractable structure act as primary ranking signals that drive citations faster than traditional SEO tactics.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/www.akarnik.com\/demo\" target=\"_blank\"><strong>See how the test lab applies these signals<\/strong><\/a> in a live walkthrough with Arjun Karnik.<\/p>\n<h2>The Share Of Voice Formula For AI Citations<\/h2>\n<p>The share of voice citation monitoring formula fits on a single line.<\/p>\n<p><strong>Share Of Voice = (Your Citations \u00f7 Total Citations Across All Brands) \u00d7 100<\/strong><\/p>\n<p>A worked calculation makes the denominator concrete. Track 50 prompts across ChatGPT, Perplexity, Gemini, and Google AI Overviews. Your brand is cited 18 times. Competitors are cited a combined 82 times. Total citations across all brands equals 100. Your share of voice equals 18 \u00f7 100, or 18%.<\/p>\n<p>The denominator is the variable most teams get wrong. It must include every citation across every tracked brand on every tracked prompt, across each engine you measure. It cannot stop at your own citations or at the prompts where you appear. <a href=\"https:\/\/aigrowthagent.co\/articles\/best-perplexity-ai-seo-tools\/\" target=\"_blank\">A Visiby benchmark based on 2,443 prompt-runs across 172 real buyer prompts found that the same brand&#8217;s citation rate diverged by up to 24 percentage points depending on the engine measured<\/a>, so the denominator must be declared per engine rather than blended across surfaces.<\/p>\n<p>The formula matches HubSpot&#8217;s definition for citation share: <a href=\"https:\/\/blog.hubspot.com\/marketing\/ai-search-kpis\" target=\"_blank\" rel=\"noindex nofollow\">your citations divided by total citations across all brands in the prompt set, multiplied by 100<\/a>. The calculation only holds when the prompt set is frozen before measurement begins and the counting rule is applied consistently across every run.<\/p>\n<h2>Citation Rate Vs Mention Rate: The Four Metrics You Need<\/h2>\n<p>Every ranking page on this topic blends these four numbers into one score or uses the terms interchangeably. That practice produces a metric that cannot be acted on. The four metrics measure different things and require different fixes when they move in the wrong direction. The table below separates the four metrics by definition, formula, and trigger condition so you can see which one your reporting actually measures.<\/p>\n<table>\n<thead>\n<tr>\n<th>Metric<\/th>\n<th>One-Line Definition<\/th>\n<th>Formula<\/th>\n<th>When To Prioritize<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Citation Rate<\/td>\n<td>The share of tracked prompts where your brand is cited as a source.<\/td>\n<td>Cited answers \u00f7 eligible tracked answers<\/td>\n<td>When below 30%<\/td>\n<\/tr>\n<tr>\n<td>Mention Rate<\/td>\n<td>The share of tracked prompts where your brand is named in the answer, whether or not it is cited as a source.<\/td>\n<td>Answers mentioning brand \u00f7 total valid answers<\/td>\n<td>When citation rate is healthy but mentions lag<\/td>\n<\/tr>\n<tr>\n<td>Citation Share<\/td>\n<td>Your citations as a proportion of all citations across the tracked prompt set, including competitors.<\/td>\n<td>Your citations \u00f7 total citations across all brands<\/td>\n<td>When comparing against competitors<\/td>\n<\/tr>\n<tr>\n<td>Citation Position<\/td>\n<td>Where your citation appears relative to competitors within a single answer.<\/td>\n<td>Position in ordered source list<\/td>\n<td>When cited but not first<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>The distinction between citation rate and mention rate drives most content decisions. <a href=\"https:\/\/botrank.ai\/glossary\/citation-rate\" target=\"_blank\" rel=\"noindex nofollow\">A high mention count with a low citation rate often indicates a brand is being talked about without being treated as a primary source.<\/a> The AI knows the brand exists, likely from training data, but does not have a specific page it considers citable enough to link back to. The fix focuses on content structure and extractability, including answer-first formatting and schema, rather than on brand awareness campaigns.<\/p>\n<p><a href=\"https:\/\/amicited.com\/reviews\/citation-rate-vs-mention-rate\" target=\"_blank\" rel=\"noindex nofollow\">Brands with rising mention rates typically see citation growth four to eight weeks later<\/a>, which positions mention rate as a leading indicator of citation potential. Track both metrics, and prioritize citation rate first when it sits below 30%.<\/p>\n<p>Citation position matters on surfaces where readers scan sources in order. <a href=\"https:\/\/useomnia.com\/blog\/ai-citation-tracking\" target=\"_blank\" rel=\"noindex nofollow\">In Perplexity, where sources appear as a numbered sidebar list, being cited first is meaningfully better than being cited fourth.<\/a> A brand that earns citations but consistently appears third or fourth behind competitors has a position problem. Position and citation problems require different interventions.<\/p>\n<p>Pick one term per concept and keep it consistent. Inconsistent terminology across a reporting period corrupts trend data and makes board-level comparisons impossible.<\/p>\n<h2>How To Monitor Share Of Voice Across AI Search<\/h2>\n<p>Once you have the four metrics defined, the next step is deciding where and how to collect them. Monitoring share of voice citation monitoring requires four named surfaces, three measurement instruments, and five operational rules applied before the first prompt is run.<\/p>\n<p>The four surfaces to track are:<\/p>\n<ul>\n<li>ChatGPT<\/li>\n<li>Google AI Overviews<\/li>\n<li>Perplexity<\/li>\n<li>Gemini<\/li>\n<\/ul>\n<p>Each engine retrieves, reranks, and displays citations differently. <a href=\"https:\/\/quickseo.ai\/blog\/ai-citation-patterns-chatgpt-claude-gemini-perplexity\" target=\"_blank\" rel=\"noindex nofollow\">ChatGPT runs on the Bing index, Perplexity on its own Sonar pipeline, and Gemini and Google AI Overviews on the Google index via query fan-out.<\/a> A blended number across all four hides surface-level gaps. A brand invisible on ChatGPT but well-cited on Perplexity needs a different fix than a brand invisible on both.<\/p>\n<p>The three measurement instruments are:<\/p>\n<ul>\n<li>Google Search Console for impressions and decay curves<\/li>\n<li>Analytics segmentation for chatgpt.com and equivalent AI referrers<\/li>\n<li>Citation, mention, and share-of-voice tracking across all four surfaces<\/li>\n<\/ul>\n<p>The five operational rules create clean data before measurement begins:<\/p>\n<ul>\n<li>Freeze your prompt set before you start. A prompt set that drifts between measurement periods makes trend data unreadable, so lock the list and version it before the first run.<\/li>\n<li>Declare which engines you are measuring. Because each engine is a separate data series, a blended number hides the surface-level gaps you need to act on.<\/li>\n<li>Preserve raw answers for audit. A screenshot of one run is an anecdote, and raw logs across runs form a measurement.<\/li>\n<li>Define your citation event and publish your denominator. A citation is a source URL attributed to a specific page, and a mention without a link counts as a different event.<\/li>\n<li>Separate citation from recommendation. Being cited as a source and being recommended as a vendor represent different outcomes with different commercial weight.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/aigrowthagent.co\/articles\/ai-search-visibility-strategy\/\" target=\"_blank\">AI Growth Agent clients average more than 12,000 additional AI citations and mentions, over 100,000 additional bot visits, and a 20% or greater lift in impressions across the first twelve weeks.<\/a><\/p>\n<p>Content freshness functions as a primary ranking signal in this channel, not as routine maintenance. <a href=\"https:\/\/www.citeflow.io\/blog\/content-freshness-perplexity-citation\" target=\"_blank\" rel=\"noindex nofollow\">According to ConvertMate&#8217;s Perplexity Visibility Study, content freshness accounts for 40% of Perplexity&#8217;s ranking factors.<\/a> Other studies estimate the weight differently. <a href=\"https:\/\/www.citeflow.io\/blog\/content-freshness-perplexity-citation\" target=\"_blank\" rel=\"noindex nofollow\">SE Ranking&#8217;s 216,524-page study puts it at ~44.2%, and Georion&#8217;s analysis lists it at 28%.<\/a> <a href=\"https:\/\/betteraisearch.com\/tactics\/content-freshness-ai-search\" target=\"_blank\" rel=\"noindex nofollow\">According to an ALM Corp analysis of 1.2 million ChatGPT responses, 30-day-old content received 3.2\u00d7 more citations than content over 90 days old, controlling for other variables.<\/a> A separate AirOps study found that pages aged 30\u201389 days achieved the highest citation rate at 32.8%, while content under 30 days old hit only 25.3%. <a href=\"https:\/\/deepsmith.ai\/blog\/content-freshness-ai-search-citations\" target=\"_blank\" rel=\"noindex nofollow\">A 2026 Seer Interactive study of 7,683 pages and 47,097 citations across ChatGPT, Gemini, and Perplexity from March to June 2026 found that 75% of cited pages had been updated within the last year.<\/a> The page refreshed most recently beats the page written most carefully when the written page has gone stale.<\/p>\n<h2>The Prompt-Level Drill-Down: Turning Measurement Into A Content Roadmap<\/h2>\n<p>Freshness tells you when to refresh, but not what to write. Share of voice citation monitoring produces a number. The prompt-level drill-down turns that number into a content production queue.<\/p>\n<p>The method has four steps:<\/p>\n<ol>\n<li>Run your frozen prompt set across all four surfaces.<\/li>\n<li>Log which brand is cited for each prompt on each surface.<\/li>\n<li>Flag every prompt where a competitor appears and you do not.<\/li>\n<li>Group those gap prompts by theme.<\/li>\n<\/ol>\n<p>Each gap prompt becomes a content brief. Each content brief targets the fan-out queries underneath the prompt, the hidden sub-queries the AI engine runs to construct its answer. Each page is then structured to match the language the machine is retrieving against. That means three things: the answer stated in the first 40 to 60 words of the relevant section, question-based headings aligned to buyer language, and schema markup applied throughout.<\/p>\n<p>This step turns a static report into a roadmap. Most share of voice reporting stops at the number. The prompt-level drill-down converts the measurement into a prioritized list of pages to write or rewrite, ranked by the commercial value of the prompts where you are currently absent.<\/p>\n<p><a href=\"https:\/\/aigrowthagent.co\/articles\/ai-search-visibility-strategy\/\" target=\"_blank\">Adding statistics increases AI citation visibility by around 31 to 33% and adding quotations by around 41 to 43%, according to the Princeton GEO study.<\/a> Those changes adjust structure rather than prose quality. The prompt-level drill-down identifies which pages need those structural changes most urgently.<\/p>\n<h2>What Counts As A Good Share Of Voice Percentage?<\/h2>\n<p>A good share of voice percentage depends on your category, your prompt set, and your competitor list. The number that matters is the one produced by your specific prompt set, measured against your specific competitor set, on your specific surfaces. Three variables move the baseline before any optimization work begins.<\/p>\n<p>The first variable is category competitiveness. Competitive categories with many established vendors have lower baseline citation share for every participant. A 10% share in a category with eight well-cited competitors represents a stronger position than a 10% share in a category with two.<\/p>\n<p>The second variable is prompt set composition. Broad category prompts such as \u201cwhat is the best CRM for enterprise\u201d are harder to earn citations on than narrow comparison prompts that already name your product. A prompt set weighted toward broad prompts will produce lower citation share numbers than one weighted toward comparison prompts, even for the same brand.<\/p>\n<p>The third variable is competitor count. More competitors mean lower share for everyone at baseline. The relevant benchmark is your share relative to the next closest competitor on the same prompt set, rather than an absolute percentage.<\/p>\n<p><a href=\"https:\/\/aigrowthagent.co\/articles\/best-perplexity-ai-seo-tools\/\" target=\"_blank\">A competitive share of citation for B2B brands in 2026 sits between 5% and 15% aggregate across major AI engines, with 20% or above signaling category leadership.<\/a> Use those figures as orientation. The actionable benchmark is your own trend line and your gap to the leading competitor on your frozen prompt set.<\/p>\n<h2>Share Of Voice Vs Share Of Search<\/h2>\n<p>Share of voice citation monitoring and share of search describe different parts of the funnel and do not translate into each other.<\/p>\n<p>Share of search measures your brand&#8217;s share of search volume relative to competitors. It acts as a demand signal that shows how often buyers search for your brand name compared to competitor brand names. It also serves as a leading indicator of category interest and brand awareness.<\/p>\n<p>Share of voice citation monitoring measures your brand&#8217;s share of citations in AI answers relative to competitors. It acts as an answer-layer presence signal that shows how often your brand appears as a cited source when buyers ask AI assistants questions in your category.<\/p>\n<p>A brand can have high share of search and low share of voice citation monitoring. That pattern appears when the brand has strong demand but content that is not structured for AI retrieval. Buyers are searching, yet they do not find the brand in the AI answers that shape their vendor shortlist before they search.<\/p>\n<p>The reverse pattern also appears: low share of search and rising share of voice citation monitoring. Challengers that invest in GEO before their brand name accumulates search volume often follow this curve. <a href=\"https:\/\/learn.g2.com\/your-buyers-are-using-ai-to-find-software.-heres-what-theyre-trusting\" target=\"_blank\" rel=\"noindex nofollow\">33% of B2B software buyers purchased from a vendor they had not previously heard of, based on what an AI assistant told them.<\/a> Share of voice citation monitoring captures that dynamic. Share of search does not.<\/p>\n<h2>What Arjun Karnik&#8217;s Tests Showed<\/h2>\n<p>On Arjun&#8217;s own site, in his own test lab, two documented tests produced controlled results that competitors have not published.<\/p>\n<p>The first test mapped fan-out queries extracted directly from ChatGPT, the hidden sub-queries the engine runs underneath a single buyer prompt, and rewrote URLs, titles, H1s, and H2s to match that language. Pages rewritten to match extracted ChatGPT fan-out queries earned citations while control pages did not. The control pages were held back specifically to isolate the effect of the language alignment rather than the content itself.<\/p>\n<p>The second test relabelled a jargon page to buyer language. A page titled in practitioner terminology was retitled, re-slugged, and restructured using the words buyers actually use when asking AI assistants for help. Citations followed within weeks of that specific change.<\/p>\n<p>In Arjun&#8217;s tests, pages dropped 78% to 99% in two months without updates. That decay remains invisible in a monthly report. By the time it surfaces, the citation position has already gone. The system he runs via AI Growth Agent (disclosed: Arjun is a partner) uses impression-decay tripwires that auto-queue refreshes when performance drops, so the library does not decay in place between audits.<\/p>\n<p>New articles on Arjun&#8217;s own site reached thousands of monthly Google impressions within weeks. The GEO subfolder went from zero to the only source of new impressions on the entire domain in 60 days. These figures come from his own Google Search Console and are labelled as such.<\/p>\n<h2>How The Test Lab Approach Compares To Other Options<\/h2>\n<p>Arjun Karnik runs a public test lab under his own name. He is a twenty-year tech marketer and former B2B software CMO who documents exactly what gets a business mentioned, cited, and recommended in AI answers, and he publishes the receipts, misses included. The method is self-verifying: ask an AI assistant about these topics and see who gets cited.<\/p>\n<p>The system runs via AI Growth Agent (disclosed partnership) at 5 to 8 autonomous actions a day, combining new articles with updates to existing ones. Because impression-decay tripwires monitor performance and auto-queue refreshes when pages start falling, the content repairs itself on a loop rather than waiting for a quarterly audit. That loop depends on two inputs: fan-out queries extracted directly from ChatGPT and mapped to a production queue, and buyer language built into URLs, titles, H1s, and H2s. Schema markup is applied throughout so the retrieval layer can parse each page.<\/p>\n<p>Alternative approaches break on some combination of volume, structure, and freshness. Traditional SEO agencies optimize for rankings on a list buyers are no longer reading. Human content agencies produce well-written pages that remain unstructured and unrefreshed. Cheap AI content delivers volume without question mapping or maintenance. GEO tools report on the problem without doing the work. A do-it-yourself approach fails the arithmetic, because one person cannot publish and refresh at machine cadence in a business with zero to three marketers.<\/p>\n<p>The test lab&#8217;s incentive is to be right in public, which is why the same system producing the visibility can be checked independently. Readers can compare the documented method against live AI answers and see whether the claims hold.<\/p>\n<p><a href=\"https:\/\/www.akarnik.com\/demo\" target=\"_blank\"><strong>See the measurement setup in action<\/strong><\/a> with Arjun Karnik&#8217;s test lab, including the prompt-level drill-down and the content system that runs it.<\/p>\n<h2>Frequently Asked Questions<\/h2>\n<h3>What Is The Difference Between Citation Rate And Mention Rate?<\/h3>\n<p>Citation rate is the share of tracked prompts where your brand is cited as a source, meaning the AI engine attributes a specific URL from your site as the basis for a claim in its answer. Mention rate is the share of tracked prompts where your brand is named in the answer, whether or not it is cited as a source. A brand can have a high mention rate and a low citation rate simultaneously. That combination means the AI knows the brand exists but does not trust its content enough to use it as a source. The fix focuses on content structure and extractability, including answer-first formatting, schema markup, and extractable answer blocks, rather than on brand awareness work. The two metrics require different optimization strategies and should never be reported as a single blended number.<\/p>\n<h3>How Do I Calculate My Share Of Voice?<\/h3>\n<p>Share Of Voice = (Your Citations \u00f7 Total Citations Across All Brands) \u00d7 100. The steps are: build a frozen prompt set of 20 to 50 prompts, declare which engines you are measuring, run the prompt set and log every citation across every brand on every surface, count your citations, count all competitor citations, and divide your citations by the total. The denominator must include every citation across every tracked brand, not just your own. Declare the denominator before you start and keep it fixed during the measurement period. Run each prompt multiple times per engine before recording a result, because AI answers are non-deterministic and a single run counts as an anecdote rather than a measurement.<\/p>\n<h3>What Is A Good Share Of Voice Percentage?<\/h3>\n<p>There is no universal number. As the benchmark section above explains, the baseline moves with category competitiveness, prompt set composition, and competitor count. The actionable benchmark is your own trend line and your gap to the leading competitor on your frozen prompt set.<\/p>\n<h3>How Long Until I See Results From Share Of Voice Citation Monitoring?<\/h3>\n<p>Coverage and impressions typically appear within weeks. Citations follow in one to three months. Compounding begins after month three. The timeline depends on how much technical plumbing needs to be fixed first. AI crawlers blocked, pages not machine-parseable, and missing schema all delay the entire downstream sequence. Content aligned to fan-out query language and structured for extractability earns citations faster than content optimized for traditional keyword rankings. In Arjun&#8217;s own test lab, the fan-out alignment test described above produced citations within weeks, while control pages did not.<\/p>\n<h3>What Do I Need To Set Up Before I Start Monitoring?<\/h3>\n<p>You need a frozen prompt set of 20 to 50 prompts, declared engines, preserved answer logs, and one counting rule applied consistently. Before any of that matters, the technical plumbing must be fixed. AI crawlers must be unblocked in robots.txt, schema must be in place across all pages, and pages must be machine-parseable with content appearing in raw HTML rather than requiring JavaScript rendering. If the retrieval layer cannot read the site, no content strategy produces citations. The visibility audit, which baselines current citation and mention status across ChatGPT, Perplexity, Gemini, and Google AI Overviews, comes before any content work because it establishes the starting line and identifies where competitors are already cited and you are absent.<\/p>\n<h3>What Is The Difference Between Share Of Voice And Share Of Search?<\/h3>\n<p>Share of search measures demand, and share of voice citation monitoring measures answer-layer presence. They do not translate into each other, as the comparison section above explains. Report both, but never use one as a proxy for the other.<\/p>\n<h3>Can I Track Share Of Voice Across All AI Surfaces?<\/h3>\n<p>Yes. Track ChatGPT, Google AI Overviews, Perplexity, and Gemini separately. Each engine retrieves, reranks, and displays citations differently, so each one forms its own data series. A blended number across all four hides surface-level gaps. A brand invisible on ChatGPT but well-cited on Perplexity needs a different fix than a brand invisible on both. Track each surface separately and only blend numbers once you understand the gaps.<\/p>\n<h3>Why Is My Mention Rate High But My Citation Rate Low?<\/h3>\n<p>As the metrics section above explains, this gap means the engine knows the brand but will not cite it. The fix is content architecture, not awareness. Focus on answer-first formatting with the direct answer in the first 40 to 60 words of each section, question-based headings that match buyer language, schema markup applied throughout, and clear factual statements that can be lifted and attributed without distortion.<\/p>\n<h2>Conclusion: Build The Measurement Discipline Before Your Competitors Do<\/h2>\n<p>The metric that matters has shifted. Impressions up, clicks down is the visible half of the problem. The invisible half is that AI answers are shaping vendor selection without your brand in them. Buyers arrive at sales calls already pre-educated by an assistant that named someone else.<\/p>\n<p>Share of voice citation monitoring gives you four metrics, a formula you can put in a spreadsheet, and a defensible answer to \u201cwhat is a good number\u201d that accounts for category, prompt set, and competitor count. It gives you a prompt-level drill-down that turns a measurement into a content roadmap. It also gives you a freshness loop that prevents the library from decaying in place between audits.<\/p>\n<p>The window for outsized gains is open now. Early citations become tomorrow&#8217;s settled answers. Answers gain incumbency, and the cost of entry rises as they harden. The businesses that decode the measurement discipline first are the ones that own the answer layer when it matters.<\/p>\n<p><a href=\"https:\/\/www.akarnik.com\/demo\" target=\"_blank\"><strong>Talk through your measurement setup<\/strong><\/a> with Arjun Karnik&#8217;s test lab and turn AI citations into a repeatable pipeline driver.<\/p>\n<section data-read-next=\"true\">\n<h2>Read Next<\/h2>\n<ul>\n<li><a href=\"https:\/\/www.akarnik.com\/blog\/measuring-ai-share-of-voice\" target=\"_blank\">Measuring AI Share of Voice: A 7-Step Playbook<\/a><\/li>\n<li><a href=\"https:\/\/www.akarnik.com\/blog\/measuring-citations-not-clicks\" target=\"_blank\">AI Citation Tracking for B2B: Metrics That Replace Rank<\/a><\/li>\n<li><a href=\"https:\/\/www.akarnik.com\/blog\/measure-ai-search-visibility\" target=\"_blank\">How to Measure AI Search Visibility: A Step-by-Step Guide<\/a><\/li>\n<li><a href=\"https:\/\/www.akarnik.com\/blog\/measure-ai-powered-search-performance\" target=\"_blank\">How to Measure AI-Powered Search Performance<\/a><\/li>\n<li><a href=\"https:\/\/www.akarnik.com\/blog\/ai-search-visibility-strategies-2026\" target=\"_blank\">AI Search Visibility Strategies That Earn Citations in 2026<\/a><\/li>\n<\/ul>\n<\/section>\n","protected":false},"excerpt":{"rendered":"<p>Learn how to define, calculate, and track AI citation share of voice. Arjun Karnik&#8217;s proven framework turns monitoring into a content roadmap.<\/p>\n","protected":false},"author":118,"featured_media":615,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[1],"tags":[],"class_list":["post-616","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/www.akarnik.com\/blog\/wp-json\/wp\/v2\/posts\/616","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.akarnik.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.akarnik.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"replies":[{"embeddable":true,"href":"https:\/\/www.akarnik.com\/blog\/wp-json\/wp\/v2\/comments?post=616"}],"version-history":[{"count":0,"href":"https:\/\/www.akarnik.com\/blog\/wp-json\/wp\/v2\/posts\/616\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.akarnik.com\/blog\/wp-json\/wp\/v2\/media\/615"}],"wp:attachment":[{"href":"https:\/\/www.akarnik.com\/blog\/wp-json\/wp\/v2\/media?parent=616"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.akarnik.com\/blog\/wp-json\/wp\/v2\/categories?post=616"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.akarnik.com\/blog\/wp-json\/wp\/v2\/tags?post=616"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}