Key traits of top answerers on Korean Social Q&A platforms: insights into user performance and entrepreneurial potential

The study addresses why social Q&A platforms remain valuable despite alternatives like search engines and generative AI, focusing on identifying traits of top contributors (“best answerers”) who both generate the core content and may have entrepreneurial potential. The research question is: What are the characteristics of the best answerers on Korean social Q&A platforms? Using Naver Knowledge-iN as the context, the authors posit that better answerers are those with higher rates of their answers being selected by askers. They motivate the importance of understanding these traits for platform management and for nurturing potential platform-dependent entrepreneurs who can translate expertise and reputation into commercial opportunities. Hypotheses span four perspectives: content features (length, immediacy, similarity), answerer characteristics (self-introduction detail, off-platform credentials), platform activity (answering across many fields; dual role as asker and answerer), and social endorsement (lurker likes).

The literature contrasts three primary tools for finding answers online—search engines, social Q&A platforms, and generative AI chatbots—highlighting differences in interaction style, source of knowledge, verifiability, and conversational dynamics. Social Q&A platforms involve human expertise and interactive discussion, which can provide depth and contextualization beyond AI-generated responses. Prior work shows heavy-tailed contribution patterns in Q&A communities where a small subset of answerers generate most high-quality content. Platforms therefore rank and manage these best contributors. The literature also suggests that top contributors can leverage expertise and reputation into entrepreneurial activity, including migration to paid Q&A services, positioning them as platform-dependent entrepreneurs. Drawing on dual-process theory, the selection of best answers may involve both systematic (content quality) and heuristic (author cues, reputation) processing. From this, hypotheses are articulated: longer answers (H1-1), faster responses (H1-2), and higher question–answer similarity (H1-3) increase likelihood of being a best answerer; more detailed self-introductions (H2-1) and more off-platform credentials (H2-2) enhance credibility; broader answering across fields (H3-1) and dual role activity (H3-2) improve performance; and more positive lurker reactions (H4) increase best-answer selection.

Research site: Naver Knowledge-iN (kin.naver.com), a major Korean social Q&A platform with extensive user activity and integrated free (Knowledge-iN) and paid (Naver eXpert) services. Data collection: A Python crawler collected profile and activity data for the top 1000 contributors; profiles were captured as of 2022-10-15. For activity, 100 reply posts per user were randomly sampled and their corresponding question posts collected; for users with fewer than 100 selections, all replies were collected. After excluding zero-length posts and cases with excessive date discrepancies, 903 users remained for analysis. Dependent variable: Answerer performance measured as the proportion of a user’s answers that were selected as best by askers. Independent variables: Content features: answer_len (average word count of answers; H1-1), answering_time (average time lag between question and answer creation; H1-2), similarity (average cosine similarity between question and answer texts; H1-3). Author features: profile_len (word count of self-introduction on user’s profile; H2-1), credentials (count of off-platform credentials listed; H2-2). Platform activity: answer_diversity (number of distinct fields/categories in which the user answered; H3-1), as_asker (percentage metric reflecting the user’s activity as asker and selections; H3-2). Social endorsement: lurker_likes (average number of likes/upvotes on the user’s answers; H4). Control: tenure (days from first response to data collection). Text processing and similarity: Korean NLP preprocessing used KoNLPy with MeCab morphological analyzer; documents were tokenized, vectorized (vector space model), and cosine similarity computed between question and answer pairs. Analysis: Hierarchical multiple regression (STATA MP 14.2) tested models by sequentially adding groups of predictors to assess incremental explanatory power (R² change). Descriptive statistics characterized the sample and variable distributions.

Sample characteristics: 903 best users analyzed. Mean performance (share of answers selected best) = 0.847 (SD 0.150), indicating about 85% of their answers were selected as best. Hierarchical regression results: - Model 1: Answer length positively associated with performance (β ≈ 0.014, p < 0.05; R² = 0.042; F p < 0.001). - Model 2: Answer length (β ≈ 0.014, p < 0.05) and credentials (β ≈ 0.026, p < 0.05) significant; R² = 0.054; ΔR² ≈ 0.012 (p < 0.001). - Model 3: Answer length (β ≈ 0.016, p < 0.05), similarity (β ≈ 0.087, p < 0.05), credentials (β ≈ 0.024, p < 0.05), answer_diversity (β ≈ 0.010, p < 0.05), and as_asker (β ≈ 0.052, p < 0.05) significant; R² = 0.075; ΔR² ≈ 0.021 (p < 0.001). - Model 4: Same variables remained significant with similar magnitudes (e.g., length β ≈ 0.015; similarity β ≈ 0.082; credentials β ≈ 0.025; diversity β ≈ 0.010; as_asker β ≈ 0.053; all p < 0.05); R² = 0.075; ΔR² = 0 from Model 3. Hypotheses outcomes (Table 5): - Supported: H1-1 (longer answers), H1-3 (higher Q–A similarity), H2-2 (more credentials), H3-1 (more fields answered), H3-2 (dual role activity as asker and answerer). - Not supported: H1-2 (immediacy), H2-1 (self-introduction length), H4 (lurker likes). Additional descriptive correlations showed positive associations of performance with profile_len, credentials, similarity, answer_diversity, as_asker, and tenure (varying significance levels). Overall explanatory power was modest (max R² ≈ 0.075) but significant.

Findings address the research question by identifying concrete traits associated with superior performance among top answerers. Content-wise, longer, more specific answers and high alignment with the question drive selections, underscoring the value of depth and relevance. Contrary to expectations, response immediacy did not predict performance, suggesting that on social Q&A platforms users prioritize quality and fit over speed. Regarding author cues, objective expertise signals (credentials) matter more than verbose self-descriptions, aligning with heuristic assessments of credibility. Platform activity breadth and dual-role engagement (both asking and answering) correlate with better performance, implying that versatility and community embeddedness enhance perceived value and trust. Social endorsement via likes from lurkers did not predict best-answer selection, indicating askers rely more on their own evaluation and on observable expertise cues than on crowd reactions. These insights differentiate social Q&A platforms from generative AI tools and inform platform design: emphasizing mechanisms that encourage detailed, question-aligned answers, highlight verified credentials, and incentivize cross-category participation and dual-role engagement.

This study contributes empirical evidence from a large Korean Q&A platform (Naver Knowledge-iN) on what characterizes top-performing answerers and connects these traits to their entrepreneurial potential within platform ecosystems. Key contributions include: (1) demonstrating the positive roles of answer length, Q–A similarity, verified credentials, field diversity, and dual-role activity in predicting best-answer selection among top users; (2) showing that immediacy, self-introduction verbosity, and lurker likes are not predictive; and (3) framing best answerers as potential platform-dependent entrepreneurs whose expertise and reputation can transition into monetizable services. Practical implications advise platforms to identify and support such contributors via features and incentives that promote detailed, relevant answers, surface credentials, and broaden participation across domains. Future research should use longitudinal and cross-platform/cross-cultural data, richer content-quality features, and more nuanced activity diversity metrics (e.g., entropy/Gini) to deepen understanding of performance drivers and entrepreneurial trajectories.

The study is cross-sectional, limiting causal inference; panel data would allow dynamic analysis (e.g., how selections evolve with behavior). It focuses on top users only, lacking comparison with lower-performing answerers; augmenting with broader user data could clarify differentiators. Attempts to include additional content-quality variables (depth, readability, objectivity, keyword density, topic similarity) were limited by dataset size and yielded non-significant results. Measuring activity diversity was constrained by dynamic category definitions, precluding use of entropy/Gini metrics; future work should refine category systems and incorporate certification-domain nuances. Generalizability is limited by the single-country, single-platform setting (Korea, Naver Knowledge-iN); cross-country, cross-lingual studies are needed.