Aggregating Impressions on Celebrities and their Reasons from Microblog Posts and Web Search Pages (2024)

Abstract: This paper aims to augment fans’ ability to critique and exploreinformation related to celebrities of interest. First, we collect postsfrom X (formerly Twitter) that discuss matters related to specificcelebrities. For the collection of major impressions from these posts,we employ ChatGPT as a large language model (LLM) to analyze andsummarize key sentiments. Next, based on collected impressions, wesearch for Web pages and collect the content of the top 30 ranked pagesas the source for exploring the reasons behind those impressions. Oncethe Web page content collection is complete, we collect and aggregatedetailed reasons for the impressions on the celebrities from the contentof each page. For this part, we continue to use ChatGPT, enhanced bythe retrieval augmented generation (RAG) framework, to ensure thereliability of the collected results compared to relying solely on theprior knowledge of the LLM. Evaluation results by comparing a referencethat is manually collected and aggregated reasons with those predictedby ChatGPT revealed that ChatGPT achieves high accuracy in reasoncollection and aggregation. Furthermore, we compared the performance ofChatGPT with an existing model of mT5 in reason collection and confirmedthat ChatGPT exhibits superior performance.

## introduction in recent years, social networking services (sns) such as x (formerly twitter) have become platforms where various opinions are expressed. as shown in figure 1 , a significant number of posts on these platforms contain impressions and critiques of celebrities, often triggered by events such as tv drama broadcasts, commercials, or news reports. among celebrity fans, there are individuals who have a strong interest in this type of information. for example, when an event or an incident that is related to a popular celebrity occurs, people express their own thoughts regarding those events and incidents in sns such as microblog (e.g., x) posts. since a number of those posts are distributed through sns, this makes it unexpectedly difficult to correctly identify what people actually intend to express in their posts. the reasoning behind these impressions is often implicit and can be influenced by various factors such as the stance of the writers of the posts, recently occurring related events, and the contexts provided by external sources like news articles and ads. however, such background information is not always detailed in the posts themselves. therefore, it is necessary to utilize not only the information within the posts but also external information to gain comprehensive understanding. considering those situations, this paper aims to augment fans' ability to critique and explore information related to celebrities of interest. to achieve this overall goal, we first collect posts from x that discuss matters related to specific celebrities and gather major impressions on those celebrities. our chatgpt-based approach overcomes the limitations of a previous research @xcite , allowing for a more flexible and comprehensive collection of aspects and impressions about celebrities. the details of our chatgptbased method for collecting and aggregating impressions are to be explained in section 4. this approach allows us to more effectively identify and aggregate the major impressions on celebrities' aspects from the vast amount of information available in x posts, while taking into account the context of the posts. this enables a more comprehensive and nuanced understanding of the public's perceptions of celebrities, going beyond the limitations of the previous method. we then use the corresponding pair of a celebrity's aspect and an impression as a keyword for collecting detailed information and their reasons from web pages. after selecting the keyword, we search for web pages using the keyword as a query and collect the content of the top 30 ranked pages as the source for exploring the reasons behind the impressions. once the web page content related to the keyword is collected, we explore detailed reasons for the impressions within the content of each page. for this part, we utilize chatgpt as a large language model (llm). a crucial aspect of our research is the use of rag @xcite in this reason collection process, which plays a significant role in enhancing the reliability of llm outputs. the rag framework allows llms to refer to information retrieved from external databases, thereby improving the reliability of the generated content. in this paper, we aim to enhance the reliability of the collected results by leveraging the rag framework to collect reasons for impressions based on the content of web pages, compared to relying solely on the prior knowledge of llms. we also show that chatgpt outperforms an existing model of mt5 @xcite in reason collection. the reasons for impressions obtained through this method, however, are highly duplicated and hence redundant, making it difficult for users to recognize the critiques and related information about celebrities at a glance. therefore, we categorize and rank the multiple reasons for impressions obtained for each keyword, considering the frequency of the reasons. this allows users to easily understand the reasons behind the impressions on celebrities' aspects in an aggregated ranked format, enabling the exploration of critiques and relevant information on celebrity-related topics. we employ chatgpt also for this part. the followings give the contribution of this paper: ## related work previous work on assisting information access regarding celebrities includes studies on constructing large-scale celebrity profile datasets by combining twitter and wikidata @xcite and analyzing persuasion strategies in celebrities' language use on social media to predict their influence @xcite . regarding assisting fans of celebrities, previous work includes studies on determining the relationship between celebrities and impressions in microblog posts @xcite and those on mining impressions on celebrities' aspects in microblog posts @xcite . this paper differs from those previous work in that we search web pages for reasons behind impressions on celebrities' aspects mined from microblog posts. this paper also differs from the previous work in that we employ chatgpt, a large language model, to extract impressions on celebrities' aspects from microblog posts, while the previous studies relied on other methods such as co-occurrence frequency statistics. furthermore, one of the key characteristics of this paper is the use of rag @xcite , which improves the reliability of llm-generated output by allowing reference to external information. rag allows llms to refer to information retrieved from external databases, enhancing the accuracy and trustworthiness of the generated content. in this paper, we leverage rag to collect reasons for impressions based on the content of web pages, aiming to significantly improve the reliability of the collected results compared to traditional methods that rely solely on the knowledge stored within the llms. recent studies have explored various rag applications and improvements, such as context tuning for tool retrieval and plan generation (anantha and vodianik, 2024) improving open-domain table question answering with late interaction models and joint training @xcite , few-shot multilingual image captioning without requiring supervised training @xcite , and incorporating additional components for more powerful question answering systems @xcite . other works have focused on improving zero-shot performance on low-resource languages using prompts from high-resource languages @xcite , leveraging retrieval for non-knowledge-intensive tasks with a two-stage framework @xcite , and incorporating rich answer encoding for better generation quality in knowledge-intensive tasks @xcite . chatgpt-related research also includes entity linking @xcite , and dialogue analysis @xcite , and text summarization @xcite @xcite . this paper differs in that we utilize chatgpt for both collecting and aggregating impressions on celebrities' aspects, as well as collecting and aggregating the reasons for these impressions. ## aspect, impression, and reason in this study, we define "aspect", "impression", and "reason" as follows: ## collecting impressions from x posts using chatgpt this section describes the procedure of collecting posts containing celebrity names from x, identifying posts that mention impressions on specific aspects of celebrities, and aggregating them into aspect-impression pairs using chatgpt. an overview is illustrated in figure 2 . ## reason collection/aggregation this section describes the procedure of collecting and aggregating reasons for impressions from web pages using chatgpt. section 5.1 discusses the process of selecting pairs of aspects and their corresponding impressions from those collected and aggregated in section 4, which will be used as keywords for searching for web pages. section 5.2 describes the method for searching for web pages using the selected keywords and collecting the content of the web pages. section 5.3 explains the procedure for collecting reasons for impressions from the collected web page contents and presents the results of manual evaluation. section 5.4 discusses the procedure for aggregating the collected reasons for impressions and presents the results of manual evaluation. ## conclusion in this paper, we proposed a method to augment fans of celebrities to critique and explore information concerning celebrities. we conducted evaluation on the results obtained by the proposed method by comparing them with manually collected and aggregated reasons for impressions. we also evaluated the methods for reason collection with chat-gpt and mt5, confirming that chatgpt shows higher performance. beyond mt5, we plan to compare chatgpt with larger models that have a comparable number of parameters, such as mistral large foot_6 or llama 3 70b foot_7 , to provide a more meaningful evaluation of the proposed method.