Tourist destinations are increasingly affected by travel-related information shared through social media. Drawing on dual-process theories on how individuals process information, this study examines the role of central and peripheral information processing routes in the formation of consumers' perceptions of the helpfulness of online reviews of mature destinations. We carried out a two-step process to address the perceived helpfulness of user-generated content, a sentiment analysis using advanced machine-learning techniques (deep learning), and a regression analysis. The database was 2023 comments posted on TripAdvisor about two iconic Venetian cultural attractions, St. Mark's Square (an open, free attraction) and the Doge's Palace (which charges an entry fee). Using deep-learning techniques, with logistic regression, we first identified which factors influenced whether a review received a “helpful” vote. Second, we selected those reviews which received at least one helpful vote to identify, through linear regression, the significant determinants of TripAdvisor users' voting behaviour. The results showed that reviewer expertise is influential in both free and paid-for attractions, although the impact of central cues (sentiment polarity, subjectivity, pictorial content) differs for both attractions. Our study suggests that managers should look beyond individual ratings and focus on the sentiment analysis of online reviews, which are shown to be based on the nature of the attraction (free vs. paid-for).

通过社交媒体共享的旅游相关信息越来越多地影响着旅游目的地。利用有关个人如何处理信息的双重过程理论，本研究考察了中央和外围信息处理路径在形成消费者对成熟目的地在线评论的帮助的看法中的作用。我们执行了两步过程来解决用户生成的内容的感知帮助，使用高级机器学习技术（深度学习）的情感分析以及回归分析。该数据库是2023年在TripAdvisor上发布的有关两个标志性威尼斯文化景点的评论，即圣马可广场（一个免费的开放景点）和总督宫（收取入场费）。使用深度学习技术和Logistic回归，我们首先确定哪些因素会影响评论是否获得“有益”的投票。其次，我们选择那些获得至少一票赞成的评论，通过线性回归确定TripAdvisor用户投票行为的重要决定因素。结果表明，尽管中心提示（情感极性，主观性，图片内容）对两个景点都有不同的影响，但评论者的专业知识对免费和付费景点都具有影响力。我们的研究表明，管理者应该超越个人评价，而应专注于在线评论的情感分析，事实表明，在线评论是基于吸引力的本质（免费vs.付费）。通过线性回归，TripAdvisor用户投票行为的重要决定因素。结果表明，尽管中心提示（情感极性，主观性，图片内容）对两个景点都有不同的影响，但评论者的专业知识对免费和付费景点都具有影响力。我们的研究表明，管理者应该超越个人评价，而应专注于在线评论的情感分析，事实表明，在线评论是基于吸引力的本质（免费vs.付费）。通过线性回归，TripAdvisor用户投票行为的重要决定因素。结果表明，尽管中心提示（情感极性，主观性，图片内容）对两个景点都有不同的影响，但评论者的专业知识对免费和付费景点都具有影响力。我们的研究表明，管理者应该超越个人评价，而应专注于在线评论的情感分析，事实表明，在线评论是基于吸引力的本质（免费vs.付费）。