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标签： 品牌声誉管理_en

  • Baidu GEO Public Opinion: Brand Reputation Monitoring, Response, and Generative Optimization Workflow in the AI Search Era (Practical Deep Dive)

    In the Chinese market, many users’ first impression of a brand does not come from its official website, but from Baidu search engine results pages (SERP): autocomplete suggestions, related searches, Baidu Baike/Zhidao/Tieba/Baijiahao content, news cards, third-party reviews, and complaint pages… Together, this content forms “Baidu public opinion.”

    In the past, we used SEO (Search Engine Optimization) to solve “being found and ranked.”
    Now, in the era of AI search and generative answers, we also need GEO (Generative Engine Optimization): solving “how AI understands, cites, and summarizes you.”

    Baidu GEO public opinion can be understood as:

    Using content assets and entity assets that are “retrievable, citable, and verifiable,” we can make Baidu search and generative answers more accurate, credible, and controllable when they involve the brand, thereby reducing negative misreadings and public-opinion risk while continuously improving brand trust and conversion.

    1. Concepts First: What Is “Baidu GEO Public Opinion”?

    1.1 What Does “Public Opinion” Mean in the Baidu Context?

    Traditionally, public opinion usually refers to “the formation and spread of public views.”
    But in Baidu search, public opinion is more concrete and more quantifiable:

    • The content users see when searching brand-related terms (visible information)
    • the judgments users form based on it (perceptions and emotions)
    • the spread of this content across the Baidu ecosystem and the wider web (dissemination and citation)

    In other words: Baidu public opinion = the brand image projected on Baidu SERPs.

    1.2 How SEO and GEO Divide Responsibilities in Public-Opinion Scenarios

    DimensionSEO (Search Engine Optimization)GEO (Generative Engine Optimization)
    GoalImprove indexing, rankings, clicks, and trafficImprove the probability and accuracy of “being retrieved, cited, and summarized by AI”
    BattlefieldSearch result lists, site structure, linksAI-generated answers, knowledge synthesis, citation source selection
    Key assetsHigh-quality pages, site authority, backlinks/mentionsFact pages, structured data, entity consistency, verifiable evidence, authoritative citation chains
    Public-opinion valueGet positive content “ranked”Make AI’s summary “closer to the facts you want to convey”

    1.3 The Core Formula of Baidu GEO Public Opinion

    A simple formula can help you understand it:

    Baidu public-opinion risk ≈ (negative visibility × source authority × spread speed) ÷ (fact-asset strength × positive placement × AI citability)

    What you can control is not public sentiment itself, but:

    • Which facts are seen
    • Which sources are trusted
    • Whom AI “cites” when summarizing

    2. What Exactly Makes Up Baidu Public Opinion

    Many brands only watch the “top 10 organic results,” but Baidu public opinion has more entry points than that. We suggest dividing the Baidu SERP into three layers:

    2.1 First Layer: Strong Entry Points (Most Likely to Shape the “First Impression”)

    • Autocomplete suggestions: brand name + terms such as “complaint / scam / real or fake / how is it / price / official website / phone”
    • Related searches: similar to autocomplete, but more like “secondary spread of public opinion”
    • News/video/Q&A cards: users can form judgments without clicking through to a page

    From experience: once strongly negative terms appear in autocomplete and related searches, users naturally assume “this is a widespread fact.”

    2.2 Second Layer: Semi-Official/Platform Content (Easier to Trust)

    Common examples include (perceived weight varies by category):

    • Encyclopedia entries (brand/company profiles)
    • Q&A content (“Is platform XXX reliable?”)
    • Community content (Tieba, forums)
    • Content distribution (Baijiahao and similar)

    2.3 Third Layer: Third-Party Sites and Word-of-Mouth Content (Influences Decisions)

    • Industry media, review sites, vertical forums
    • Complaint platforms, court judgments/regulatory notices (if applicable)
    • E-commerce reviews and Q&A reposted and aggregated

    The hardest part of public-opinion governance is often the third layer: you cannot “control publishers,” but you can control factual evidence and narrative control.

    3. Why AI Search Amplifies Public Opinion

    Generative search (or search with AI summaries) upgrades the public-opinion problem from “seeing a negative link” to “seeing a negative conclusion directly.”

    3.1 AI Does Not “Make Things Up”—It “Assembles Retrievable Sources”

    Most generative systems follow a similar pipeline:

    1. Retrieval: fetch highly relevant pages from search/knowledge bases
    2. Filtering: favor authoritative, clearly structured, citable content
    3. Generation: merge multiple sources into a “seemingly reasonable” conclusion

    If your brand has “thin fact assets and few authoritative citations” in the retrieval pool, while negative content is clearly structured and widely spread, AI may:

    • Use negative content as the main “background fact”
    • Use complaint/controversy terms as summary labels
    • Mistake an individual case for a widespread phenomenon (typical “over-generalization”)

    3.2 The Essence of GEO Public-Opinion Governance: Competing for the “Right to Be Cited”

    In AI search, what a brand should compete for is not “deleting all negatives,” but:

    • Making correct facts easier to retrieve
    • Making authoritative evidence easier to cite
    • Making your explanatory framework easier for AI to adopt

    4. Classifying Public Opinion and Prioritizing Response

    We suggest a four-quadrant model based on “truthfulness × impact,” which is intuitive and practical.

    QuadrantSituationPriorityResponse objectiveRecommended actions
    ATrue and high impactHighestStop the bleeding + repair trustRapid response, factual disclosure, remediation plan, continuous updates
    BFalse but high impactHighestCorrect errors + clarifyEvidence chain, official statement, platform appeals, authoritative third-party endorsement
    CTrue but low impactMediumReduce spreadCustomer-service closed loop, FAQ, localized explanation
    DFalse and low impactLowMonitor primarilyRecord, and handle if necessary

    4.1 A Practical “5-Minute Assessment”

    When you encounter negative content, answer 5 questions first:

    1. Can this content be easily verified by users?
    2. Does the publisher have platform authority/media authority?
    3. Does it appear in autocomplete/related searches/news cards?
    4. Has it already been reposted elsewhere?
    5. Does it reach core decision-makers (for example, B2B buyers, investors, job seekers)?

    The more “yes” answers, the more it should enter the A/B high-priority process.

    5. Build an Actionable Baidu Public-Opinion Monitoring System

    Without monitoring, there is no governance. Monitoring is not “glancing at search results”; it requires building an early-warning, reviewable, and quantifiable system.

    5.1 Keyword Library: Don’t Focus Only on the Brand Name

    Cover at least 6 keyword categories (the earlier, the more critical):

    1. Brand terms: brand name, abbreviation, pinyin, misspellings
    2. Product terms: brand + core product/model/feature
    3. Risk terms: complaint, scam, absconding, rights protection, quality, after-sales, real or fake, refund, etc.
    4. People/organization terms: founder/executives, full company name, sub-brands
    5. Transaction terms: price, fees, contract, invoice, authorization, agency
    6. Comparison terms: brand vs. competitor, brand + “which is better”

    You can maintain them in a table like the one below (example fields):

    KeywordCategoryImportancePrimary intentKey observation locationsOwnerUpdate frequency
    Brand name + complaintRisk term5Risk verificationAutocomplete/related searches/top 10PRDaily
    Brand name + official websiteTransaction term4ConversionBrand zone/official website placementGrowthWeekly
    Product name + reviewsProduct term4EvaluationNews/reviews/Q&AContentWeekly

    5.2 Monitoring Scope: Cover at Least “SERP + Baidu Ecosystem + Web-Wide Spread Sources”

    Suggested minimum configuration:

    • Baidu web search (desktop + mobile)
    • Baidu News/Video
    • Baidu product ecosystem (Baike/Q&A/community/content platforms)
    • Key third-party sites (media, review, complaint platforms, industry forums)

    5.3 Early-Warning Mechanism: Use “Thresholds,” Not “Gut Feeling”

    Common early-warning thresholds (adjustable by industry):

    • Negative content enters the top 10 for brand terms
    • Negative terms appear in autocomplete/related searches
    • New reposts of the same negative topic reach ≥ N within 24 hours
    • Abnormal fluctuations in brand-term search volume (using index tools)

    6. Public-Opinion Diagnosis: First Understand the Placement Structure of Baidu SERPs

    The first step in public-opinion governance is not “issuing a statement,” but “understanding your map on the SERP.”

    6.1 Build a “Placement Audit Table” (Update Weekly)

    Pull the top 20 SERP results for core brand terms and mark them as follows:

    Rank/modulePage/sourceTypeSentimentControllabilityAuthorityHandling strategy
    1Official homepageOwnedPositiveHighMediumStrengthen structure, improve clicks
    2EncyclopediaPlatformNeutralMediumHighCalibrate entity consistency, complete information
    3Media coverageThird partyNegativeLowHighCommunicate corrections/additional information, publish a fact page
    …………………

    Controllability: We suggest 3 levels: high (owned channels), medium (platforms you can operate), low (independent third parties).

    6.2 Common Negative Types and Corresponding Solutions (Plain-Language Version)

    1. Misunderstanding-type negatives: misunderstandings caused by incomplete information
    • Solution: publish “fact pages + FAQ” to make the answers easier to cite
    1. Complaint-type negatives: user complaints triggered by service/after-sales issues
    • Solution: solve the problem first, then respond publicly; on the content side, add “after-sales mechanism transparency”
    1. Incident-type negatives: accidents, regulatory issues, major controversies
    • Solution: phased disclosure (facts—response—remediation—review), continuously update pages, and keep version records
    1. Attack-type negatives: competitor smears/black PR/bulk content
    • Solution: preserve evidence + compliant complaints/legal routes + authoritative endorsement + positive placement

    7. Response Strategy: From “Delete-the-Post Thinking” to “Fact Assets + Citation Chains”

    Many teams treat public opinion as “deleting posts and suppressing it.” In the AI search era, this approach carries greater risk because:

    • Deletion is uncontrollable and costly
    • Even if deleted, AI may still cite historical caches or similar content
    • “A lack of authoritative explanation” makes negatives look more like facts

    A more effective strategy is: First build citable fact assets, then use SEO/GEO for distribution and placement.

    7.1 A Standard Public-Opinion Response Cadence (Ready to Use)

    0–6 hours: stop-the-bleeding phase

    • Align messaging: what the facts are, what is uncertain, when updates will come
    • Create an “event master page” (official site/authoritative channels) and prepare for continuous updates
    • Align internally: PR, legal, customer service, operations, growth

    6–24 hours: explanation phase

    • Publish a first statement: concise, verifiable, and non-emotional
    • Publish an FAQ in sync: answer the 5–10 questions users care about most in one place
    • External communication: contact key platforms/media to provide evidence and additional information

    24–72 hours: repair phase

    • Publish a remediation plan and timeline
    • Prove improvement with cases/data (for example, response time, return/exchange process)
    • Encourage authoritative third-party coverage/interpretation (compliant and truthful)

    7–30 days: rebuilding phase

    • Produce a “post-mortem report/transparency report”
    • Complete the “topic cluster” build (see Section 8)
    • Continuously optimize SERP placement and the AI citation chain

    8. Baidu GEO: Make AI More Willing to Cite You

    If SEO solves “visibility,” then GEO solves “citability.” The following is the most practical part.

    8.1 Build the “Three Fact-Asset Components” (Strongly Recommended)

    1. Fact Page
      Used to answer: What happened? What is the factual evidence? How can it be verified?
      Suggested content structure:
    • Event overview (timeline)
    • Key facts and evidence (images/documents/IDs/third-party proof)
    • Scope of user impact (who is affected, how it is handled)
    • Solutions and progress updates (version records)
    • Contact details and ticket entry points
    1. FAQ page (Question Hub)
      Used to capture search intent: refunds, after-sales, authenticity, authorization, qualifications, pricing, etc.
      Suggested structure:
    • Answer each question with a one-sentence conclusion
    • Then provide 3–5 key points
    • Provide “actionable steps” (phone/online entry points/document checklist)
    1. Evidence page (Evidence Library)
      Used to support authority:
    • Licenses and certificates, test reports, patents/copyrights
    • Compliance statements, privacy policy, terms of service
    • Partner/customer cases (verifiable)

    For GEO, “evidence” matters more than “rhetoric.” AI is more likely to cite content that is clearly structured, numbered, sourced, and versioned.

    8.2 Write for AI and for People: GEO Content-Writing Rules

    Recommended rules (plain-language version):

    • Lead with the conclusion: answer “is it, can it, how to” directly in the first 1–2 sentences
    • Then give evidence: use lists, data, steps, and cited sources
    • Use consistent entity names: do not keep changing how you refer to the brand name, company name, or product name
    • Reduce vague wording: use fewer “might, should, probably” and more “process/conditions/boundaries”
    • Modularize: timelines, steps, checklists, comparison tables
    • Make updates traceable: label the “last updated date” and version number

    8.3 Structured Data: Make It Easier for Search and AI to “Capture Structure”

    On official FAQ and fact pages, first ensure the user-visible content is accurate; add structured data only when the page content matches a type supported by the target consumer.
    Even though engines differ in their support for structured data, it remains an explicit “machine-readable” way to express content.

    Common type suggestions:

    • User-visible FAQ (most direct); FAQPage use only when the target consumer supports it
    • Article/NewsArticle (event updates)
    • Organization (brand entity)
    • BreadcrumbList (site structure)
    • HowTo (process-type questions: refunds, after-sales, repair requests)

    If there is a clear consumer need, the following FAQPage JSON-LD examples are for field-structure reference only; verify against current platform documentation before launch.

    8.4 The “Citation Chain” Mindset: Let Authoritative Sources Endorse You

    The key to GEO public-opinion governance is building a citation chain:

    • Official fact page (first-hand facts)
    • Authoritative media/industry associations cite and report it (third-party endorsement)
    • Encyclopedia/Q&A/platform content cites the above sources (platform diffusion)
    • When AI retrieves these highly authoritative pages, it is more inclined to cite them when generating answers

    This is far more effective than “publishing a pile of advertorials,” and more compliant.

    9. A Positive-Placement Map Within the Baidu Ecosystem

    To stabilize public opinion, you need “multiple trustworthy sources” across the Baidu ecosystem, not just the official website as a single seedling.

    9.1 Suggested Priority (High to Low)

    1. Official site and authoritative on-site pages (fact pages, FAQ, qualifications, newsroom)
    2. Encyclopedia entity pages (unified, verifiable brand/company information)
    3. Authoritative media coverage and interviews (citable and retrievable)
    4. “Verifiable answers” in Q&A and communities (less marketing, more evidence)
    5. In-depth explanatory articles on content platforms (to cover long-tail questions)

    9.2 A Replicable “Topic Cluster” Structure (Recommended for the Official Site)

    A topic cluster built around “trust” is more resilient to public-opinion shocks than “product introductions” alone:

    • /about/ (company and team)
    • /compliance/ (qualifications, compliance, privacy, terms)
    • /service/ (after-sales, returns/exchanges, tickets)
    • /news/ (news and announcements, especially event updates)
    • /faq/ (high-frequency questions)
    • /case/ (customer cases and verifiable results)

    10. Compliant Handling Paths for Negative Content

    Here we must emphasize the compliance boundary:
    The long-term solution to public-opinion governance is “error correction + transparency + improvement + placement,” not fabricating information or attacking others.

    10.1 Compliant Path 1: Communicate Corrections and Additional Information

    Applicable to: misunderstandings, missing information, citation errors
    Approach: provide evidence, request additions/corrections, and share official fact-page links

    10.2 Compliant Path 2: Platform-Rule Appeals/Complaints

    Applicable to: obvious rumors, infringement, privacy leaks, brand impersonation
    Key points:

    • Evidence must be complete (screenshots, links, timestamps)
    • Explain the specific violation (rather than making emotional statements)
    • Seek “correction/removal/labeling” rather than only deletion

    10.3 Compliant Path 3: Legal Route

    Applicable to: serious defamation, major commercial losses, malicious organized attacks
    Suggestion: legal leads, PR coordinates external messaging, and avoid “secondary spread” that amplifies impact.

    11. How to Measure Results: Public-Opinion and GEO KPIs

    Saying “it feels better” is not enough. Use a set of quantifiable metrics.

    11.1 Core Baidu Public-Opinion KPIs

    • Positive/negative share in the top 10: the positive/negative ratio among the top 10 results for brand terms
    • Number of negative terms in autocomplete/related searches: and how many days they persist
    • Changes in the authority of negative sources: whether high-authority negatives are corrected/supplemented
    • Clicks and dwell time on event pages: indicate whether users found the official explanation
    • Placement for brand conversion terms: whether terms such as “official website, phone, price, authorization” are covered by positive content

    11.2 GEO-Related KPIs (New Metrics in the AI Search Era)

    • AI answer consistency: whether AI’s summary of the brand matches the fact page (sampling checks)
    • Share of cited sources: whether pages cited in AI answers include your “fact assets”
    • Long-tail question coverage: whether question-style searches (how to/is it/how to choose) are matched by corresponding FAQ pages
    • Entity consistency: whether company name/brand name/product name/address/phone are consistent and verifiable

    12. A 90-Day Implementation Roadmap (Follow Directly)

    Days 0–7: Build the foundation (monitoring + assets)

    • [ ] Build a keyword library and monitoring table
    • [ ] Conduct a SERP placement audit (at least 20 core terms)
    • [ ] Launch a minimum version of the fact page/FAQ/evidence library
    • [ ] Unify brand entity information (name, abbreviation, contact details, qualifications)

    Days 8–30: Build placement (SEO) + citability (GEO)

    • [ ] Update fact pages and announcements weekly (with version numbers)
    • [ ] Produce 10–20 “long-tail question explainer” pieces
    • [ ] Encourage 2–5 authoritative third parties to cite your fact pages
    • [ ] Review user-visible FAQ content; launch and validate FAQPage only when the target consumer supports it

    Days 31–90: Build the system (long-term risk resilience)

    • [ ] Complete the “trust topic cluster” site structure
    • [ ] Establish a monthly public-opinion review mechanism and SOP
    • [ ] Produce a “transparency report/service data dashboard”
    • [ ] Close the loop on product/service remediation for high-frequency negative points (actually fix the problems)

    13. One-Page Checklist: Baidu GEO Public-Opinion Practice Checklist

    Monitoring

    • [ ] Keyword library (brand/product/risk/people/transaction/comparison)
    • [ ] Monitor autocomplete, related searches, and news cards separately
    • [ ] Update the top-20 SERP placement table weekly

    Assets

    • [ ] Fact page (event timeline + evidence + update records)
    • [ ] FAQ page (conclusion first + step-based)
    • [ ] Evidence library (verifiable qualifications/reports/terms/cases)

    Distribution

    • [ ] Consistent information across operable Baidu ecosystem properties (Baike/Q&A/content platforms, etc.)
    • [ ] Authoritative third-party citation chain (media/associations/experts)

    Compliance

    • [ ] Error correction first, evidence first, avoid confrontational language
    • [ ] Keep records of appeals/legal routes, avoid secondary spread

    Evaluation

    • [ ] Positive/negative share in the top 10
    • [ ] Trends of negative terms in autocomplete/related searches
    • [ ] AI answer consistency and citation-source quality

  • What Should We Do If AI Answers Contain Negative Information About Our Brand?

    After AI search gradually became a primary entry point for users to get information, brand reputation risk has taken on a new form: negative information no longer stays only on web pages, posts, or search results—it gets “summarized” into a sentence or a conclusion and appears directly in AI answers.
    The impact on companies is often faster and more concentrated: users may not click links to verify, but they will remember AI output as a “conclusion.”

    From the perspective of GEO (Generative Engine Optimization), this article provides an actionable handling process and AI search optimization methods to help you, without departing from facts and compliance boundaries, turn “negative AI answers” into a controllable problem that can be corrected, diluted, and governed over time.

    Let’s Unify the Concepts First: What Exactly Do AI Search, AI Search Optimization, and GEO Solve?

    What Is AI Search?

    AI search does not just “give you a pile of links”; it synthesizes web information, knowledge bases, user content (UGC), news reports, and more into a more “answer-like” result. It may:

    • cite external pages as evidence;
    • combine training information already inside the model;
    • “compress and summarize” content from multiple sources.

    This is also why, once negative information is “compressed into a conclusion,” its spread and harm are clearly amplified.

    What Is AI Search Optimization?

    AI search optimization focuses on: how to make AI, when answering questions related to you, more easily retrieve and understand you, cite you, and output accurate, complete, verifiable conclusions. It is not just keyword rankings but competition over “answer quality and citation quality.”

    What Is GEO (Generative Engine Optimization)?

    GEO (Generative Engine Optimization) can be understood as content and brand entity optimization for generative engines (LLMs/AI search). Its goal is to make your brand information appear in AI output as:

    • verifiable (sourced and citable)
    • understandable (clear structure, explicit definitions)
    • repeatable (concise conclusions + evidence chains)
    • sustainable (stable over time and updatable)

    Why Does AI Produce Negative Information About Your Brand? (Four Most Common Causes)

    To solve “what AI said,” you first need to understand “why AI said it.”

    1) AI cites external sources (news, forums, reviews, social media)

    AI may retrieve and cite:

    • news reports (including old news)
    • forums/post bars/Q&A sites
    • negative reviews or complaints on review platforms
    • secondary spread on social media, even rumors

    If you can locate the specific source, you can reduce the problem from “abstract AI” to “a specific piece of content or a group of content.”

    2) AI amplifies an individual incident into a general conclusion

    For example:

    • one customer dispute → “this company has terrible service”
    • one forum discussion → “they are going bankrupt”
    • a piece of outdated information → “they have ceased operations”

    A common risk of generative answers is “over-generalization,” which is a typical GEO challenge: AI summarizes well but is insensitive to boundaries and timeliness.

    3) Information is outdated but still repeated (update lag)

    Even if you have clarified or remediated:

    • old content may still be indexed and cited;
    • new content, if not authoritative or clear enough, may not be preferentially adopted by AI;
    • platform update cadences and crawl cadences differ, so “you have corrected it, but AI still outputs the old conclusion.”

    4) The brand lacks “fact anchors,” forcing AI to piece together third-party material

    When your official site does not have:

    • a clear brand introduction (About/company information)
    • citable fact pages
    • authoritative, verifiable update notes
    • citable FAQ/statement pages

    AI will rely more on third-party sources, where negative content is more likely to be “narrative and spreadable.”

    GEO Challenges and Misconceptions: Why Do Many Companies Become Less Effective the More They Handle It?

    The following are high-frequency misconceptions that cause you to invest a lot of effort while AI output does not change—or even worsens.

    Misconception 1: Focus only on traditional SEO rankings and ignore the “AI citation chain”

    Traditional SEO emphasizes “rankings and traffic,” while AI search emphasizes “citations and repeatable conclusions.”
    Even if you rank well, AI may not cite you if the page lacks clear conclusions and evidence chains.

    Misconception 2: Only delete posts without building “official citable fact sources”

    Deletion or appeals are indeed important, but what matters more is completing citable official information.
    Otherwise, even if one negative source disappears, AI may still piece together similar conclusions from other sources.

    Misconception 3: Use “emotional rebuttal” instead of “factual clarification”

    AI more easily absorbs structured, verifiable content with clear wording.
    Emotional, aggressive rebuttals without evidence neither improve credibility nor help AI citation.

    Misconception 4: Turn GEO into “keyword stuffing”

    In generative engines, “repeating keywords” does not naturally become a “trustworthy answer.”
    GEO values more:

    • whether definitions are clear
    • whether evidence is credible
    • whether the structure is easy to cite
    • whether entity information is consistent (company name, aliases, product names, responsible persons, etc.)

    Misconception 5: Ignore “positive clarification of negative keywords”

    Many companies are unwilling to mention words such as “bankruptcy/absconding/complaints” on their official sites, but the reality is:
    users will ask, and AI will answer.
    If you do not provide clear clarification, AI can only cite third-party narratives.

    An Actionable Handling Process: Turn “Negative AI Answers” into a Solvable Task List

    Below is a GEO handling loop for corporate teams. You can treat it as a “reputation emergency plan for the AI search era.”

    Step 1: Reproduce and Collect Evidence (Turn the Problem from “Feeling” into “Evidence”)

    You need to fully record:

    • which AI product/platform was used
    • how the question was asked (Prompt)
    • the answer content (original text)
    • whether there were citation links/sources
    • time and region (if they differ)
    • differences across phrasings of the same question

    Why does this matter?
    Because AI search answers may change with phrasing. You need to lock down “the phrasing that triggers the negative conclusion” before you can fix it in a targeted way.

    It is recommended to build an internal table with fields:

    • question type (bankruptcy/complaint/true-false/competitor comparison, etc.)
    • negative conclusion summary
    • cited sources (URL/platform/account)
    • severity level
    • owner
    • handling status

    Step 2: Identify the Information Source (Locate “Where the Negative AI Statement Comes From”)

    Split the negative answer into two parts:

    1. conclusion: what AI said (for example, “was once complained about for quality issues”)
    2. basis: why AI said it (which sources it cited or what content it inferred)

    Common source-tracing methods (by priority):

    • first check whether AI provides citation links
    • use key sentences to locate the original text via search engines/site search
    • check high-authority third parties (media, forums, encyclopedias, review platforms)
    • check the “secondary spread chain” (reposts, screenshots, copies)

    The goal is not “debating AI” but finding “governable sources.”

    Step 3: Classify and Match the Handling Strategy (Fact/Misunderstanding/Rumor/Outdated)

    Classifying negative information into 4 types greatly improves handling efficiency:

    TypeCharacteristicsYour goalCore action
    Factual negativegenuinely happened, with evidenceexplain and improve rather than denypublic statement + remediation evidence + customer success cases
    Misunderstanding negativepartly true but exaggerated/generalizedcorrect boundaries and semanticsfact clarification page + FAQ + unified external messaging
    Rumor/defamationno evidence, unreliable sourcesclean up sources + build authoritative counter-evidenceappeal/legal letter (if necessary) + authoritative statement page
    Outdated informationold incident or old statusupdate timeliness and current statusupdate note + fact anchor “as of a certain date”

    Step 4: Content Response (Publish Formal Clarification in an “AI-Citable Way”)

    In the GEO context, “responding” is not posting a social media update but creating retrievable, citable, long-term updatable page assets.

    Two types of pages are recommended:

    A. “Fact Statement / Clarification” page (for a single rumor)

    Page titles should directly match user questions:

    • “Fact Statement Regarding ‘X’”
    • “Clarification and Information Update on the ‘X Rumor’”

    Suggested content structure (AI-citation friendly):

    1. one-sentence conclusion: clear stance (for example: the rumor is false/remediated/resolved)
    2. fact timeline: what happened, when, and how it was handled
    3. evidence and citations: publicly shareable materials (announcements, reports, media coverage, screenshot descriptions, etc.)
    4. current status: the latest business/operations/qualification status as of a certain date
    5. external contact channels: media contact/customer service/official email
    6. update record: record the date and content of each update

    B. “Brand Facts” page (long-term asset)

    This is one of your most important “authority anchors” in the AI search era. It does not chase hot topics; it only answers “factual questions AI will be repeatedly asked.”

    Example modules:

    • company/brand introduction (one paragraph + 3 key points)
    • operating status (as of date)
    • core products and target audiences
    • compliance qualifications and certifications (if any)
    • customers and cases (the publicly shareable part)
    • clarification of common misunderstandings (fact-based responses including keywords such as bankruptcy/absconding/complaints)
    • media coverage and newsroom entry
    • official channel list (official site, WeChat official account, video account, etc.)

    Step 5: Optimize the Brand Introduction (the About Page Must Be “Directly Citable by AI”)

    You need to upgrade the About page from a “brand story” to a “brand entity specification.”

    Suggested writing (better for AI search optimization):

    • one-paragraph definition: who we are + what problem we solve for whom
    • three facts: founding time/operating status/service scope, etc. (provide “as of date” where possible)
    • key data (use exaggeration cautiously; make it verifiable where possible)
    • clarification of common doubts (turn negative keywords into factual statements)
    • For example: “As of 2025, the team continues to operate and provide services to X type of customers” (please use your actual latest date here).
    • authority endorsement entry: newsroom/white papers/cases/media coverage pages

    This is essentially GEO: letting AI get “the most repeatable answer” from “the most authoritative source.”

    Step 6: Contact Platforms and Govern Sources (Correction Requests + Deletion/Appeals)

    When negative information is clearly wrong, rumor, or defamation, handling must include “source governance”:

    • For AI platforms: submit corrections through their “feedback/report error/appeal” channels
      When submitting, include:
    • screenshots of the wrong conclusion
    • links to authoritative pages with correct facts (your statement page/fact repository)
    • an explanation of why it is wrong (outdated/miscited/no evidence)
    • For source platforms (forums, social media, review platforms, etc.): file appeals, complaints, or deletion/correction requests according to platform rules

    Note:
    your goal is not “no negative content on the internet” but:

    • fewer misinformation items
    • more authoritative information
    • AI more inclined to cite authoritative information and output accurate conclusions

    Step 7: Actively Optimize Positive Assets (Counter Negatives with Positives, but Truthfully)

    AI output is often a “weighted summary of materials.” When positive, authoritative, verifiable material is sufficient, negative content—even if it exists—is less likely to become the main conclusion.

    What you should do is not “buy fake reviews” but build publicly shareable, verifiable content assets, such as:

    • customer success cases (anonymized or industry-level treatment)
    • product quality/service process descriptions (transparency reduces misunderstanding)
    • team/author/expert endorsement pages (enhance credibility)
    • newsroom and regular updates (build time-series authority)
    • industry viewpoint content (bind the brand to professional topics)

    Step 8: Establish Continuous Monitoring (Extend Public-Opinion Monitoring to AI Output)

    In the AI search era, you need to monitor two lines:

    1. Content line: what new content about your brand appears online (especially high-authority citable sources)
    2. Output line: how AI describes you on key questions (accuracy, cited sources, sentiment)

    It is recommended to build an “AI question bank” (continuous retesting):

    • Is the brand bankrupt/absconding?
    • How is the brand reviewed? Are there complaints?
    • How does it compare with competitors?
    • Is it trustworthy/a scam?
    • How is after-sales service?
    • Is it suitable for a certain audience?

    Fix several phrasings for each question and periodically sample output and citation changes.

    WordPress Implementation Checklist: Turn GEO into Executable On-Site Page Engineering

    If you publish with WordPress (as your site also does), treat the following as “GEO infrastructure”:

    1) Pages you must build (priority high to low)

    • About/company information page (citable, with fact anchors)
    • Brand Facts repository
    • Newsroom/media coverage aggregation page
    • Fact statement/clarification pages (created per incident)
    • FAQ (for high-frequency user questions)
    • Contact us (official channels)
    • Cases/customer stories (publicly shareable part)

    2) Structural writing (make it easier for AI to “grab conclusions”)

    • titles directly match user questions (do not write only marketing slogans)
    • provide a “conclusion paragraph” at the start of each page (1–3 sentences)
    • use subheadings, lists, and tables to chunk information
    • provide “as of dates” for key facts
    • clearly label external citations and evidence (where publicly shareable)

    3) “Citation-Friendly Paragraph” Template for AI Search Optimization

    You can insert similar paragraphs on key pages (replace with actual facts):

    Conclusion: the statement about “X” is inaccurate.
    Facts: as of [date], we continue to operate and provide [services/products] to [customer type].
    Basis: see the “Update Record” and “Evidence Materials” sections of this page for related notes and updates.
    To verify: further information is available through [official contact].

    This writing is clear for human readers and easier for AI to extract as a “repeatable answer.”

    Practical Examples: How to Handle High-Risk Negative Keywords Such as “Bankruptcy/Absconding/Complaints”?

    Scenario A: AI says you have “gone bankrupt”

    Possible causes:

    • old posts/old news
    • a company address change being misread
    • short-term business adjustments being exaggerated

    Recommended actions:

    1. add “operating status (as of date)” to the Brand Facts repository and About page
    2. publish an “Operating Status Statement/Information Update” page (with contact details)
    3. if there is a source rumor, initiate deletion/correction appeals
    4. use the newsroom/announcements to create continuous updates and build “time-series credibility”

    Scenario B: AI says you have “received many complaints”

    Possible causes:

    • concentrated negative reviews on one platform
    • individual cases being secondarily spread
    • competitor comparison content steering

    Recommended actions:

    1. first distinguish “factual complaints” from “exaggerated misunderstanding”
    2. for genuine issues: publicly disclose improvement measures (process, refunds, after-sales)
    3. for misunderstandings: publish a fact clarification page emphasizing boundaries and data sources
    4. long term: build cases, product transparency pages, and service standard pages to dilute single-point negatives

    One-Page Action Checklist (for Internal Team Alignment)

    • [ ] Reproduce and record the negative AI answer (platform, prompt, original text, citations, time)
    • [ ] Trace sources: locate the source content and spread chain behind the negative conclusion
    • [ ] Classify: fact/misunderstanding/rumor/outdated → match the handling strategy
    • [ ] Build pages: fact statement/clarification (citable structure + evidence chain + update records)
    • [ ] Strengthen About: add fact anchors and fact-based responses to common doubts
    • [ ] Build a “brand fact repository”: form a long-term authoritative information source
    • [ ] Platform corrections: submit corrections/appeals to AI platforms and source platforms
    • [ ] Positive assets: continuously update the newsroom, cases, transparency pages, and FAQs
    • [ ] Monitoring: build an AI question bank and periodically retest output and citation changes

    Conclusion: AI Is Not “Deliberately Smearing You”—It Is Merely Retelling the Material It Can Get

    It is not uncommon for AI to output negative information, especially when third-party content is easier to retrieve and more narratively compelling, while your official information lacks a “citable structure.”
    From the perspective of generative engine optimization (GEO), the core of handling such issues is not “arguing with AI” but:

    • making authoritative facts more visible (retrievable)
    • making facts easier to cite (extractable)
    • keeping facts continuously updated (traceable)
    • governing the sources of errors (cleanable)

    Only when you change the “information supply” will AI’s conclusions change accordingly. A company’s PR and content strategy must also incorporate AI search as a new information channel into systematic management—this is now a basic capability for brand growth and risk control.