The vast amount of social media and web data offers valuable insights for purposes such as brand reputation management, topic research, competitive analysis, product development, and public opinion surveys. However, analysing these data to identify patterns and extract valuable insights is challenging due to the vast number of posts, which can number in the thousands within a single day. One practical approach is topic clustering, which creates clusters of mentions that refer to a specific topic. Following this process will create several manageable clusters, each containing hundreds or thousands of posts. These clusters offer a more meaningful overview of the discussed topics, eliminating the need to categorise each post manually. Several topic detection algorithms can achieve clustering of posts, such as LDA, NMF, BERTopic, etc. The existing algorithms, however, have several important drawbacks, including language constraints and slow or resource-intensive data processing. Moreover, the labels for the clusters typically consist of a few keywords that may not make sense unless one explores the mentions within the cluster. Recently, with the introduction of AI large language models, such as GPT-4, new techniques can be realised for topic clustering to address the aforementioned issues. Our novel approach (AI Mention Clustering) employs LLMs at its core to produce an algorithm for efficient and accurate topic clustering of web and social data. Our solution was tested on social and web data and compared to the popular existing algorithm of BERTopic, demonstrating superior resource efficiency and absolute accuracy of clustered documents. Furthermore, it produces summaries of the clusters that are easily understood by humans instead of just representative keywords. This approach enhances the productivity of social and web data researchers by providing more meaningful and interpretable results.

Social media generate massive amounts of unstructured, multilingual textual data that contain many sentiments, emotions and opinions from users that provide insights for enterprises undergoing digital transformation. In this paper, we evaluate a variety of commercially available, lightweight Large Language Models (LLMs) for sentiment, emotion, aspect-based sentiment, and toxic speech detection on noisy, real-world social media datasets. These lightweight models are compared (against traditional RoBERTa-based classifiers and heavyweight “pro” LLMs) in terms of performance, latency, and cost trade-offs that are key for scalable deployment across the enterprise. Our results show that lightweight LLMs achieve good accuracy with considerably lower response times and costs, which makes them suitable as the next step in digital transformation for real-time social media analytics. Additionally, we investigate the importance of prompt engineering and show that preprocessing can be very limited for LLMs. This study provides tactical knowledge for researchers and developers seeking to integrate LLM based social media analytics into digitized business practices and evaluates the intersection of operational performance and resource efficiency.

The wealth of data available in social media and the web can be valuable for many purposes, including brand reputation management, topic research, competitive analysis, product development, public opinion surveys and many more. However, analyzing this data to identify patterns and extract useful conclusions can be challenging due to the volume of collected posts, which can be several thousands, even for a single day. One useful approach is the use of topic clustering, to create clusters of mentions that refer to a specific topic. Doing so, will result in a number of manageable clusters that can contain hundreds or thousands of posts. With these clusters, one can now have a much more meaningful overview of the topics being discussed, instead of having to go through each post and manually categorize them into topics. There are several topic detections algorithms that can achieve clustering of posts, such as LDA, NMF, BERTopic etc. The existing algorithms however have several important drawbacks, including language constraints and slow or resource-intensive processing of data. More importantly, the label for the clusters is usually comprised of a few keywords, that do not make much sense unless someone browses through the mentions inside the cluster. Recently, with the introduction of AI Large Language Models, such as GPT-4, new techniques can be realized for topic clustering, to address the aforementioned issues. Our novel approach (AI Mention Clustering) employs LLMs at its core, to produce an algorithm for efficient and accurate topic clustering of web and social data. Our solution was tested on actual social and web data and was compared to the popular existing algorithm of BERTopic, demonstrating superior resource efficiency and absolute accuracy of clustered documents. Furthermore, it produces summaries of the clusters and are easily understood by humans, instead of just representative keywords. This way, it makes the work of a researcher of social & web data, faster, easier and more productive.

Social media generates massive amounts of unstructured, multilingual textual data that contain many sentiments, emotions, and opinions from users that provide insights for enterprises undergoing digital transformation. In this paper, we evaluate a variety of commercially available, lightweight large language models (LLMs) for sentiment, emotion, aspect-based sentiment, and toxic speech detection on noisy, real-world social media datasets. These lightweight models are compared (against traditional RoBERTa-based classifiers and heavyweight “pro” LLMs) in terms of performance, latency, and cost trade-offs that are key for scalable deployment across the enterprise. Our results show that lightweight LLMs achieve good accuracy with considerably lower response times and costs, which makes them suitable as the next step in digital transformation for real-time social media analytics. Additionally, we investigate the importance of prompt engineering and show that preprocessing can be very limited for LLMs. This study provides operational guidelines regarding model selection based on latency, accuracy, and cost trade-offs as well as optimal prompt engineering techniques. Finally, it evaluates the intersection of operational performance and resource efficiency to help researchers and developers integrate LLM-based social media analytics into digitized business practices.

Traditional Earned Media Value (EMV) calculations suffer from methodological limitations by depending on oversimplified and outdated proxies like Advertising Value Equivalency (AVE). In service marketing, where offerings are intangible and consumers cannot inspect an offering before purchase, trust relies heavily on authentic influencer communication and peer advocacy; these measurement flaws are therefore especially problematic. This paper introduces two innovations tailored for the modern media ecosystem: (1) Mention Quality and Impact (MQI), a transparent scoring system (1–10 scale) that evaluates sentiment, engagement, credibility, and AI visibility, with a bounded adjustment for conversion intent; and (2) Mention Earned Media Value (mEMV), a CPM-based valuation model that integrates MQI with platform-specific benchmarks. By accounting for participation inequality, influencer dynamics, and the rise of virtual search environments, this framework advances media measurement for service brands toward greater accuracy, transparency, and scalability.