The volatility and unpredictability of emerging technologies, such as artificial intelligence (AI), generate significant uncertainty, which is widely discussed on social media. This study examines anticipatory discourse surrounding technological futures by analysing 1.5 million posts from 400 key opinion leaders (KOLs) published on the X platform (from 2021 to 2023). Using advanced text mining techniques, including BERTopic modelling, sentiment, emotion, and attitude analyses, the research identifies 100 distinct topics reflecting anticipated tech-driven futures. Our findings emphasize the dual role of KOLs in framing \textit{present futures} -- optimistic visions of transformative technologies like AI and IoT -- and influencing \textit{future presents}, where these projections shape contemporary societal and geopolitical debates. Positive emotions such as Hope dominate, outweighing Anxiety, particularly in topics like ``Machine Learning, Data Science, and Deep Learning,'' while discussions around ``Climate Change'' and ``War, Ukraine, and Trump People'' elicit \textit{Anxiety}. By framing technologies as solutions to societal challenges, KOLs act as mediators of societal narratives, bridging imagined futures and current realities. These insights underscore their pivotal role in directing public attention with emerging technologies during periods of heightened uncertainty, advancing our understanding of anticipatory discourse in technology-mediated contexts.

Solving partial or ordinary differential equation models in cardiac electrophysiology is a computationally demanding task, particularly when high-resolution meshes are required to capture the complex dynamics of the heart. Moreover, in clinical applications, it is essential to employ computational tools that provide only relevant information, ensuring clarity and ease of interpretation. In this work, we exploit two recently proposed operator learning approaches, namely Fourier Neural Operators (FNO) and Kernel Operator Learning (KOL), to learn the operator mapping the applied stimulus in the physical domain into the activation and repolarization time distributions. These data-driven methods are evaluated on synthetic 2D and 3D domains, as well as on a physiologically realistic left ventricle geometry. Notably, while the learned map between the applied current and activation time has its modelling counterpart in the Eikonal model, no equivalent partial differential equation (PDE) model is known for the map between the applied current and repolarization time. Our results demonstrate that both FNO and KOL approaches are robust to hyperparameter choices and computationally efficient compared to traditional PDE-based Monodomain models. These findings highlight the potential use of these surrogate operators to accelerate cardiac simulations and facilitate their clinical integration.

People worldwide are currently confronted with a number of technological challenges, which act as a potent source of uncertainty. The uncertainty arising from the volatility and unpredictability of technology (such as AI) and its potential consequences is widely discussed on social media. This study uses BERTopic modelling along with sentiment and emotion analysis on 1.5 million tweets from 2021 to 2023 to identify anticipated tech-driven futures and capture the emotions communicated by 400 key opinion leaders (KOLs). Findings indicate positive sentiment significantly outweighs negative, with a prevailing dominance of positive anticipatory emotions. Specifically, the 'Hope' score is approximately 10.33\% higher than the median 'Anxiety' score. KOLs emphasize 'Optimism' and benefits over 'Pessimism' and challenges. The study emphasizes the important role KOLs play in shaping future visions through anticipatory discourse and emotional tone during times of technological uncertainty.

Large language models~(LLMs) have demonstrated impressive performance in various applications, among which role-playing language agents (RPLAs) have engaged a broad user base. Now, there is a growing demand for RPLAs that represent Key Opinion Leaders (KOLs), \ie, Internet celebrities who shape the trends and opinions in their domains. However, research in this line remains underexplored. In this paper, we hence introduce MINDECHO, a comprehensive framework for the development and evaluation of KOL RPLAs. MINDECHO collects KOL data from Internet video transcripts in various professional fields, and synthesizes their conversations leveraging GPT-4. Then, the conversations and the transcripts are used for individualized model training and inference-time retrieval, respectively. Our evaluation covers both general dimensions (\ie, knowledge and tones) and fan-centric dimensions for KOLs. Extensive experiments validate the effectiveness of MINDECHO in developing and evaluating KOL RPLAs.

Indonesian Ministry of Finance had endorsed 10 Key Opinion Leaders to help promoting government retail bonds SBR011 during selling period of 25 May-16 June 2022. This study analyzed effectiveness of the endorsement by using several indicators; engagement rate, enthusiasm rate and sentiment analysis of feedbacks from KOL audiens. Data was gathered from social media Instagram and TikTok social platform used by the KOL to post their marketing contents. This paper found that the endorsement is quite effective to promote the SBR011 and yields mostly positive feedback on the marketing campaign. Definisi Key Opinion Leader (KOL) Menurut influencermarketinghub, KOL dideskripsikan sebagai "person or organization who has expert product knowledge and influence in a respective field. They are trusted by relevant interest groups and have significant effects on consumer behavior" [7].

A series of senior European MPs have been approached in recent days by individuals who appear to be using deepfake filters to imitate Russian opposition figures during video calls. Those tricked include Rihards Kols, who chairs the foreign affairs committee of Latvia's parliament, as well as MPs from Estonia and Lithuania. Tom Tugendhat, the chair of the UK foreign affairs select committee, has also said he was targeted. "Putin's Kremlin is so weak and frightened of the strength of @navalny they're conducting fake meetings to discredit the Navalny team," Tugendhat posted in a tweet, referring to the Russian opposition leader Alexei Navalny. "They got through to me today. They won't broadcast the bits where I call Putin a murderer and thief, so I'll put it here."

By now, pretty much everybody agrees that artificial intelligence (AI) will change all of our lives forever. From transportation to quality control, from education to medicine, from entertainment to manufacturing, few if any area of our professional and personal lives will remain unaltered. It does not come as a big surprise then, that AI will also have a profound impact on the work of Medical Science Liaisons (MSLs). Tasks like identifying and selecting the best key opinion leaders is something supervised machine learning algorithms excel at. These algorithms use labeled examples and apply what they learn from those to predict future events.

The phrase "opinion leader" became popular after Paul Lazarsfeld and Elihu Katz wrote about it in their 1955 book Personal Influence: The Part Played by People in the Flow of Mass Communications. In their book, the authors describe opinion leaders as influential people who further circulate media messages through face-to-face communication within their own personal networks. In today's pharmaceutical landscape, KOLs (Key Opinion Leaders) are primarily valued for their level of influence. Pharma companies leverage the influence of expert physicians and researchers in order to recruit patients for clinical trials and influence the adoption of drugs at regional, national, and global scales. KOLs are so important that they cost pharma companies just under one-third of their total marketing expenditures.