나는 남자다운 여자를 좋아한다. 거기 안에 어느정도의 강함도 포함되어 있지만, 무던한것도 이제는 포함하고 싶다. 무던한게 좀 중요한 이유가 요즘에 내 성격이 지랄맞은 편이라고 자각하는 중인데(눈 깜빡하면 도지는 멘헤라병), 이 지랄병을 받아줄 사람은 무던한 사람밖에 없다고 느꼈다. 나 같은 사람 두명이서 만나면 반드시 파국을 맞이하게 될테니. 무던한 사람은 재미가 없지만 발상의 전환을 해보기로 했다. 무던한 사람들의 새하얀 캔버스를 내 개지랄로 그냥 싹다 덮어버리는 그 상상을 해보니까 무던한 사람들이 좀 좋아졌다. 내가 말도 행동도 서슴없이 하는 편이라 개의치 안할 그런 사람이 또 필요한것 같기도 하고. 그리고 그 무던한 멘탈마저 개지랄로 털어버려서 재밌는 반응이 나오면 희열 느낄듯.

고양이들이 스크래쳐가 필요한것 처럼 나는 나의 개지랄을 받아줄 사람이 필요하다. 내 인간 스크래쳐는 어디?

Freundinnen & Freunde der Weisheit, willkommen zur zweiten Ausgabe des wöchentlichen EpicMonday-Newsletters!

Achtsamkeit gilt vielen als Schlüssel zu innerer Ruhe, Selbstoptimierung und seelischer Balance. Doch neue Studien zeigen: Meditieren ist kein Allheilmittel – und kann unter bestimmten Bedingungen sogar unerwünschte Nebenwirkungen haben. Wer etwa Schuldgefühle „wegmeditiert“, könnte weniger bereit sein, Verantwortung zu übernehmen oder anderen zu helfen. Der westliche Trend zur Achtsamkeit als Effizienztechnik blendet häufig aus, dass die ursprüngliche Praxis auf Mitgefühl, Einsicht und ethisches Handeln zielt – nicht auf Leistungssteigerung.

Gerade im Kontext von Individualismus birgt Achtsamkeit die Gefahr, gesellschaftliche Probleme zur privaten „Kopfsache“ zu machen: Stress wird nicht strukturell hinterfragt, sondern innerlich reguliert. Das kann dazu führen, dass Betroffene sich an krankmachende Arbeitsbedingungen anpassen, statt sie zu verändern. Psychologen warnen davor, Achtsamkeit mit passivem Erdulden zu verwechseln. Bewusstsein soll nicht betäuben, sondern ermächtigen – vorausgesetzt, sie wird mit der richtigen Haltung praktiziert.

Hinzu kommt: Achtsamkeit kann psychisch belastend sein. In Studien berichteten Teilnehmende mit Depressionen von Ängsten, Schlafstörungen oder wiederkehrenden Traumata während Achtsamkeitsprogrammen. Besonders vulnerable Gruppen brauchen deshalb erfahrene Begleitung. Die zentrale Erkenntnis lautet: Achtsamkeit kann heilsam sein – aber nur, wenn sie eingebettet ist in ein klares ethisches Verständnis, behutsam angeleitet wird und nicht instrumentalisiert wird, um Menschen still an belastende Verhältnisse anzupassen.

Denkanstoss zum Wochenbeginn

„Seien Sie ordentlich und adrett im Leben, damit Sie ungestüm und originell in Ihrer Arbei sein können.“ – Gustave Flaubert (1821–1880)

ProductivityPorn-Tipp der Woche: Priorisieren

Lerne, Deine Aufgaben konsequent zu priorisieren. Ohne ein klares System verlierst Du Dich schnell in unwichtigen Tätigkeiten. Ob Du mit einer numerischen Bewertung oder Farbcodes arbeitest – Hauptsache, Du setzt Prioritäten und hältst Dich daran.

Aus dem Archiv: Ziele setzen mit Harry Frankfurt

Frankfurts Vorlesungen bieten tiefgehende Einsichten, die weit über die Philosophie hinausreichen und praktische Anwendungen im Alltag finden können. Seine Überlegungen zum Willen, zur Bedeutung von Zielen und zur Rolle der Liebe geben wertvolle Impulse auch für das Setzen persönlicher Ziele.

weiterlesen …

Vielen Dank, dass Du Dir die Zeit genommen hast, diesen Newsletter zu lesen. Ich hoffe, die Inhalte konnten Dich inspirieren und Dir wertvolle Impulse für Dein (digitales) Leben geben. Bleib neugierig und hinterfrage, was Dir begegnet!

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EpicMind – Weisheiten für das digitale Leben „EpicMind“ (kurz für „Epicurean Mindset“) ist mein Blog und Newsletter, der sich den Themen Lernen, Produktivität, Selbstmanagement und Technologie widmet – alles gewürzt mit einer Prise Philosophie.

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Disclaimer Teile dieses Texts wurden mit Deepl Write (Korrektorat und Lektorat) überarbeitet. Für die Recherche in den erwähnten Werken/Quellen und in meinen Notizen wurde NotebookLM von Google verwendet. Das Artikel-Bild wurde mit ChatGPT erstellt und anschliessend nachbearbeitet.

Topic #Newsletter

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De las luciérnagas se habla poco.

Hoy las recordé.

Cuando era niño me sorprendía verlas por la noche, en un pequeño bosque húmedo cerca de mi casa: Bosques de Prusia.

Nunca maté a ninguna.

Ni siquiera por la curiosidad que tenía de entender cómo encendían su luz. Me contuve. Creo que siempre he tenido compasión por la vida, y sobre todo por una tan frágil como la de una luciérnaga.

Ya no las vemos porque llenamos todo de luz.

Casi no quedan espacios oscuros y húmedos, dos cosas que ellas necesitan:

la humedad para vivir

y la oscuridad para que su luz pueda ser vista por las demás.

También es lamentable que, al intentar controlar otras plagas con pesticidas, ellas se hayan ido alejando hacia zonas menos invadidas por la urbanidad.

Están escondidas, como muchas cosas buenas.

Hay luces que solo existen en la oscuridad.

Cuando la eliminamos, no perdemos a los insectos:

perdemos la capacidad de ver lo frágil.

Me gustaría ir a acampar y verlas nuevamente.

Prometo estar en la oscuridad.

Envíame tu comentario, lo leeré con calma →

https://tally.so/r/2EEedb

Nota de autor:

Bosques de Prusia era una pequeña area boscosa atrás de la colonia de mi casa, en Los Santos. En ese bosque estaba la cancha, una loma o dos, un par de ríos. Realmente se sentía como salir de la ciudad.

i don’t have to tell anyone anything. if they wanted to know, they would ask, and i would answer. i am a vessel and a receiver. i am just going to wait and respond to what fills me with joy and excitement. if it’s aligned, it will happen effortlessly. if it’s not, it will fall apart. i will let it be what it is.

Somewhere in the digital ether, a trend is being born. It might start as a handful of TikTok videos, a cluster of Reddit threads, or a sudden uptick in Google searches. Individually, these signals are weak, partial, and easily dismissed as noise. But taken together, properly fused and weighted, they could represent the next viral phenomenon, an emerging public health crisis, or a shift in consumer behaviour that will reshape an entire industry.

The challenge of detecting these nascent trends before they explode into the mainstream has become one of the most consequential problems in modern data science. It sits at the intersection of signal processing, machine learning, and information retrieval, drawing on decades of research originally developed for radar systems and sensor networks. And it raises fundamental questions about how we should balance the competing demands of recency and authority, of speed and accuracy, of catching the next big thing before it happens versus crying wolf when nothing is there.

The Anatomy of a Weak Signal

To understand how algorithms fuse weak signals, you first need to understand what makes a signal weak. In the context of trend detection, a weak signal is any piece of evidence that, on its own, fails to meet the threshold for statistical significance. A single tweet mentioning a new cryptocurrency might be meaningless. Ten tweets from unrelated accounts in different time zones start to look interesting. A hundred tweets, combined with rising Google search volume and increased Reddit activity, begins to look like something worth investigating.

The core insight driving modern multi-platform trend detection is that weak signals from diverse, independent sources can be combined to produce strong evidence. This principle, formalised in various mathematical frameworks, has roots stretching back to the mid-twentieth century. The Kalman filter, developed by Rudolf Kalman in 1960, provided one of the first rigorous approaches to fusing noisy sensor data over time. Originally designed for aerospace navigation, Kalman filtering has since been applied to everything from autonomous vehicles to financial market prediction.

According to research published in the EURASIP Journal on Advances in Signal Processing, the integration of multi-modal sensors has become essential for continuous and reliable navigation, with articles spanning detection methods, estimation algorithms, signal optimisation, and the application of machine learning for enhancing accuracy. The same principles apply to social media trend detection: by treating different platforms as different sensors, each with its own noise characteristics and biases, algorithms can triangulate the truth from multiple imperfect measurements.

The Mathematical Foundations of Signal Fusion

Several algorithmic frameworks have proven particularly effective for fusing weak signals across platforms. Each brings its own strengths and trade-offs, and understanding these differences is crucial for anyone attempting to build or evaluate a trend detection system.

Kalman Filtering and Its Extensions

The Kalman filter remains one of the most widely used approaches to sensor fusion, and for good reason. As noted in research from the University of Cambridge, Kalman filtering is the best-known recursive least mean-square algorithm for optimally estimating the unknown states of a dynamic system. The Linear Kalman Filter highlights its importance in merging data from multiple sensors, making it ideal for estimating states in dynamic systems by reducing noise in measurements and processes.

For trend detection, the system state might represent the true level of interest in a topic, while the measurements are the noisy observations from different platforms. Consider a practical example: an algorithm tracking interest in a new fitness app might receive signals from Twitter mentions (noisy, high volume), Instagram hashtags (visual, engagement-focused), and Google search trends (intent-driven, lower noise). The Kalman filter maintains an estimate of both the current state and the uncertainty in that estimate, updating both as new data arrives. This allows the algorithm to weight recent observations more heavily when they come from reliable sources, and to discount noisy measurements that conflict with the established pattern.

However, traditional Kalman filters assume linear dynamics and Gaussian noise, assumptions that often break down in social media environments where viral explosions and sudden crashes are the norm rather than the exception. Researchers have developed numerous extensions to address these limitations. The Extended Kalman Filter handles non-linear dynamics through linearisation, while Particle Filters (also known as Sequential Monte Carlo Methods) can handle arbitrary noise distributions by representing uncertainty through a population of weighted samples.

Research published in Quality and Reliability Engineering International demonstrates that a well-calibrated Linear Kalman Filter can accurately capture essential features in measured signals, successfully integrating indications from both current and historical observations. These findings provide valuable insights for trend detection applications.

Dempster-Shafer Evidence Theory

While Kalman filters excel at fusing continuous measurements, many trend detection scenarios involve categorical or uncertain evidence. Here, Dempster-Shafer theory offers a powerful alternative. Introduced by Arthur Dempster in the context of statistical inference and later developed by Glenn Shafer into a general framework for modelling epistemic uncertainty, this mathematical theory of evidence allows algorithms to combine evidence from different sources and arrive at a degree of belief that accounts for all available evidence.

Unlike traditional probability theory, which requires probability assignments to be complete and precise, Dempster-Shafer theory explicitly represents ignorance and uncertainty. This is particularly valuable when signals from different platforms are contradictory or incomplete. As noted in academic literature, the theory allows one to combine evidence from different sources while accounting for the uncertainty inherent in each.

In social media applications, researchers have deployed Dempster-Shafer frameworks for trust and distrust prediction, devising evidence prototypes based on inducing factors that improve the reliability of evidence features. The approach simplifies the complexity of establishing Basic Belief Assignments, which represent the strength of evidence supporting different hypotheses. For trend detection, this means an algorithm can express high belief that a topic is trending, high disbelief, or significant uncertainty when the evidence is ambiguous.

Bayesian Inference and Probabilistic Fusion

Bayesian methods provide perhaps the most intuitive framework for understanding signal fusion. According to research from iMerit, Bayesian inference gives us a mathematical way to update predictions when new information becomes available. The framework involves several components: a prior representing initial beliefs, a likelihood model for each data source, and a posterior that combines prior knowledge with observed evidence according to Bayes' rule.

For multi-platform trend detection, the prior might encode historical patterns of topic emergence, such as the observation that technology trends often begin on Twitter and Hacker News before spreading to mainstream platforms. The likelihood functions would model how different platforms generate signals about trending topics, accounting for each platform's unique characteristics. The posterior would then represent the algorithm's current belief about whether a trend is emerging. Multi-sensor fusion assumes that sensor errors are independent, which allows the likelihoods from each source to be combined multiplicatively, dramatically increasing confidence when multiple independent sources agree.

Bayesian Networks extend this framework by representing conditional dependencies between variables using directed graphs. Research from the engineering department at Cambridge University notes that autonomous vehicles interpret sensor data using Bayesian networks, allowing them to anticipate moving obstacles quickly and adjust their routes. The same principles can be applied to trend detection, where the network structure encodes relationships between platform signals, topic categories, and trend probabilities.

Ensemble Methods and Weak Learner Combination

Machine learning offers another perspective on signal fusion through ensemble methods. As explained in research from Springer and others, ensemble learning employs multiple machine learning algorithms to train several models (so-called weak classifiers), whose results are combined using different voting strategies to produce superior results compared to any individual algorithm used alone.

The fundamental insight is that a collection of weak learners, each with poor predictive ability on its own, can be combined into a model with high accuracy and low variance. Key techniques include Bagging, where weak classifiers are trained on different random subsets of data; AdaBoost, which adjusts weights for previously misclassified samples; Random Forests, trained across different feature dimensions; and Gradient Boosting, which sequentially reduces residuals from previous classifiers.

For trend detection, different classifiers might specialise in different platforms or signal types. One model might excel at detecting emerging hashtags on Twitter, another at identifying rising search queries, and a third at spotting viral content on TikTok. By combining their predictions through weighted voting or stacking, the ensemble can achieve detection capabilities that none could achieve alone.

The Recency and Authority Trade-off

Perhaps no question in trend detection is more contentious than how to balance recency against authority. A brand new post from an unknown account might contain breaking information about an emerging trend, but it might also be spam, misinformation, or simply wrong. A post from an established authority, verified over years of reliable reporting, carries more weight but may be slower to identify new phenomena.

Why Speed Matters in Detection

Speed matters enormously in trend detection. As documented in Twitter's official trend detection whitepaper, the algorithm is designed to search for the sudden appearance of a topic in large volume. The algorithmic formula prefers stories of the moment to enduring hashtags, ignoring topics that are popular over a long period of time. Trending topics are driven by real-time spikes in tweet volume around specific subjects, not just overall popularity.

Research on information retrieval ranking confirms that when AI models face tie-breaking scenarios between equally authoritative sources, recency takes precedence. The assumption is that newer data reflects current understanding or developments. This approach is particularly important for news-sensitive queries, where stale information may be not just suboptimal but actively harmful.

Time-based weighting typically employs exponential decay functions. As explained in research from Rutgers University, the class of functions f(a) = exp(-λa) for λ greater than zero has been used for many applications. For a given interval of time, the value shrinks by a constant factor. This might mean that each piece of evidence loses half its weight every hour, or every day, depending on the application domain. The mathematical elegance of exponential decay is that the decayed sum can be efficiently computed by multiplying the previous sum by an appropriate factor and adding the weight of new arrivals.

The Stabilising Force of Authority

Yet recency alone is dangerous. As noted in research on AI ranking systems, source credibility functions as a multiplier in ranking algorithms. A moderately relevant answer from a highly credible source often outranks a perfectly appropriate response from questionable origins. This approach reflects the principle that reliable information with minor gaps proves more valuable than comprehensive but untrustworthy content.

The PageRank algorithm, developed by Larry Page and Sergey Brin in 1998, formalised this intuition for web search. PageRank measures webpage importance based on incoming links and the credibility of the source providing those links. The algorithm introduced link analysis, making the web feel more like a democratic system where votes from credible sources carried more weight. Not all votes are equal; a link from a higher-authority page is stronger than one from a lower-authority page.

Extensions to PageRank have made it topic-sensitive, avoiding the problem of heavily linked pages getting highly ranked for queries where they have no particular authority. Pages considered important in some subject domains may not be important in others.

Adaptive Weighting Strategies

The most sophisticated trend detection systems do not apply fixed weights to recency and authority. Instead, they adapt their weighting based on context. For breaking news queries, recency dominates. For evergreen topics, authority takes precedence. For technical questions, domain-specific expertise matters most.

Modern retrieval systems increasingly use metadata filtering to navigate this balance. As noted in research on RAG systems, integrating metadata filtering effectively enhances retrieval by utilising structured attributes such as publication date, authorship, and source credibility. This allows for the exclusion of outdated or low-quality information while emphasising sources with established reliability.

One particularly promising approach combines semantic similarity with a half-life recency prior. Research from ArXiv demonstrates a fused score that is a convex combination of these factors, preserving timestamps alongside document embeddings and using them in complementary ways. When users implicitly want the latest information, a half-life prior elevates recent, on-topic evidence without discarding older canonical sources.

Validating Fused Signals Against Ground Truth

Detecting trends is worthless if the detections are unreliable. Any practical trend detection system must be validated against ground truth, and this validation presents its own formidable challenges.

Establishing Ground Truth for Trend Detection

Ground truth data provides the accurately labelled, verified information needed to train and validate machine learning models. According to IBM, ground truth represents the gold standard of accurate data, enabling data scientists to evaluate model performance by comparing outputs to the correct answer based on real-world observations.

For trend detection, establishing ground truth is particularly challenging. What counts as a trend? When exactly did it start? How do we know a trend was real if it was detected early, before it became obvious? These definitional questions have no universally accepted answers, and different definitions lead to different ground truth datasets.

One approach uses retrospective labelling: waiting until the future has happened, then looking back to identify which topics actually became trends. This provides clean ground truth but cannot evaluate a system's ability to detect trends early, since by definition the labels are only available after the fact.

Another approach uses expert annotation: asking human evaluators to judge whether particular signals represent emerging trends. This can provide earlier labels but introduces subjectivity and disagreement. Research on ground truth data notes that data labelling tasks requiring human judgement can be subjective, with different annotators interpreting data differently and leading to inconsistencies.

A third approach uses external validation: comparing detected trends against search data, sales figures, or market share changes. According to industry analysis from Synthesio, although trend prediction primarily requires social data, it is incomplete without considering behavioural data as well. The strength and influence of a trend can be validated by considering search data for intent, or sales data for impact.

Metrics That Matter for Evaluation

Once ground truth is established, standard classification metrics apply. As documented in Twitter's trend detection research, two metrics fundamental to trend detection are the true positive rate (the fraction of real trends correctly detected) and the false positive rate (the fraction of non-trends incorrectly flagged as trends).

The Receiver Operating Characteristic (ROC) curve plots true positive rate against false positive rate at various detection thresholds. The Area Under the ROC Curve (AUC) provides a single number summarising detection performance across all thresholds. However, as noted in Twitter's documentation, these performance metrics cannot be simultaneously optimised. Researchers wishing to identify emerging changes with high confidence that they are not detecting random fluctuations will necessarily have low recall for real trends.

The F1 score offers another popular metric, balancing precision (the fraction of detected trends that are real) against recall (the fraction of real trends that are detected). However, the optimal balance between precision and recall depends entirely on the costs of false positives versus false negatives in the specific application context.

Cross-Validation and Robustness Testing

Cross-validation provides a way to assess how well a detection system will generalise to new data. As noted in research on misinformation detection, cross-validation aims to test the model's ability to correctly predict new data that was not used in its training, showing the model's generalisation error and performance on unseen data. K-fold cross-validation is one of the most popular approaches.

Beyond statistical validation, robustness testing examines whether the system performs consistently across different conditions. Does it work equally well for different topic categories? Different platforms? Different time periods? Different geographic regions? A system that performs brilliantly on historical data but fails on the specific conditions it will encounter in production is worthless.

Acceptable False Positive Rates Across Business Use Cases

The tolerance for false positives varies enormously across applications. A spam filter cannot afford many false positives, since each legitimate message incorrectly flagged disrupts user experience and erodes trust. A fraud detection system, conversely, may tolerate many false positives to ensure it catches actual fraud. Understanding these trade-offs is essential for calibrating any trend detection system.

Spam Filtering and Content Moderation

For spam filtering, industry standards are well established. According to research from Virus Bulletin, a 90% spam catch rate combined with a false positive rate of less than 1% is generally considered good. An example filter might receive 7,000 spam messages and 3,000 legitimate messages in a test. If it correctly identifies 6,930 of the spam messages, it has a false negative rate of 1%; if it misses three of the legitimate messages, its false positive rate is 0.1%.

The asymmetry matters. As noted in Process Software's research, organisations consider legitimate messages incorrectly identified as spam a much larger problem than the occasional spam message that sneaks through. False positives can cost organisations from $25 to $110 per user each year in lost productivity and missed communications.

Fraud Detection and Financial Applications

Fraud detection presents a starkly different picture. According to industry research compiled by FraudNet, the ideal false positive rate is as close to zero as possible, but realistically, it will never be zero. Industry benchmarks vary significantly depending on sector, region, and fraud tolerance.

Remarkably, a survey of 20 banks and broker-dealers found that over 70% of respondents reported false positive rates above 25% in compliance alert systems. This extraordinarily high rate is tolerated because the cost of missing actual fraud, in terms of financial loss, regulatory penalties, and reputational damage, far exceeds the cost of investigating false alarms.

The key insight from Ravelin's research is that the most important benchmark is your own historical data and the impact on customer lifetime value. A common goal is to keep the rate of false positives well below the rate of actual fraud.

Marketing and Consumer Trends

For marketing applications, the calculus shifts again. Detecting an emerging trend early can provide competitive advantage, but acting on a false positive (by launching a campaign for a trend that fizzles) wastes resources and may damage brand credibility.

Research on the False Discovery Rate (FDR) from Columbia University notes that a popular allowable rate for false discoveries is 10%, though this is not directly comparable to traditional significance levels. An FDR of 5% means that among all signals called significant, 5% are truly null, representing an acceptable level of noise for many marketing applications where the cost of missing a trend exceeds the cost of investigating false leads.

Health Surveillance and Public Safety

Public health surveillance represents perhaps the most consequential application of trend detection. Detecting an emerging disease outbreak early can save lives; missing it can cost them. Yet frequent false alarms can lead to alert fatigue, where warnings are ignored because they have cried wolf too often.

Research on signal detection in medical contexts from the National Institutes of Health emphasises that there are important considerations for signal detection and evaluation, including the complexity of establishing causal relationships between signals and outcomes. Safety signals can take many forms, and the tools required to interrogate them are equally diverse.

Cybersecurity and Threat Detection

Cybersecurity applications face their own unique trade-offs. According to Check Point Software, high false positive rates can overwhelm security teams, waste resources, and lead to alert fatigue. Managing false positives and minimising their rate is essential for maintaining efficient security processes.

The challenge is compounded by adversarial dynamics. Attackers actively try to evade detection, meaning that systems optimised for current attack patterns may fail against novel threats. SecuML's documentation on detection performance notes that the False Discovery Rate makes more sense than the False Positive Rate from an operational point of view, revealing the proportion of security operators' time wasted analysing meaningless alerts.

Techniques for Reducing False Positives

Several techniques can reduce false positive rates without proportionally reducing true positive rates. These approaches form the practical toolkit for building reliable trend detection systems.

Multi-Stage Filtering

Rather than making a single pass decision, multi-stage systems apply increasingly stringent filters to candidate trends. The first stage might be highly sensitive, catching nearly all potential trends but also many false positives. Subsequent stages apply more expensive but more accurate analysis to this reduced set, gradually winnowing false positives while retaining true detections.

This approach is particularly valuable when the cost of detailed analysis is high. Cheap, fast initial filters can eliminate the obvious non-trends, reserving expensive computation or human review for borderline cases.

Confirmation Across Platforms

False positives on one platform may not appear on others. By requiring confirmation across multiple independent platforms, systems can dramatically reduce false positive rates. If a topic is trending on Twitter but shows no activity on Reddit, Facebook, or Google Trends, it is more likely to be platform-specific noise than a genuine emerging phenomenon.

This cross-platform confirmation is the essence of signal fusion. Research on multimodal event detection from Springer notes that with the rise of shared multimedia content on social media networks, available datasets have become increasingly heterogeneous, and several multimodal techniques for detecting events have emerged.

Temporal Consistency Requirements

Genuine trends typically persist and grow over time. Requiring detected signals to maintain their trajectory over multiple time windows can filter out transient spikes that represent noise rather than signal.

The challenge is that this approach adds latency to detection. Waiting to confirm persistence means waiting to report, and in fast-moving domains this delay may be unacceptable. The optimal temporal window depends on the application: breaking news detection requires minutes, while consumer trend analysis may allow days or weeks.

Contextual Analysis Through Natural Language Processing

Not all signals are created equal. A spike in mentions of a pharmaceutical company might represent an emerging health trend, or it might represent routine earnings announcements. Contextual analysis (understanding what is being said rather than just that something is being said) can distinguish meaningful signals from noise.

Natural language processing techniques, including sentiment analysis and topic modelling, can characterise the nature of detected signals. Research on fake news detection from PMC notes the importance of identifying nuanced contexts and reducing false positives through sentiment analysis combined with classifier techniques.

The Essential Role of Human Judgement

Despite all the algorithmic sophistication, human judgement remains essential in trend detection. Algorithms can identify anomalies, but humans must decide whether those anomalies matter.

The most effective systems combine algorithmic detection with human curation. Algorithms surface potential trends quickly and at scale, flagging signals that merit attention. Human analysts then investigate the flagged signals, applying domain expertise and contextual knowledge that algorithms cannot replicate.

This human-in-the-loop approach also provides a mechanism for continuous improvement. When analysts mark algorithmic detections as true or false positives, those labels can be fed back into the system as training data, gradually improving performance over time.

Research on early detection of promoted campaigns from EPJ Data Science notes that an advantage of continuous class scores is that researchers can tune the classification threshold to achieve a desired balance between precision and recall. False negative errors are often considered the most costly for a detection system, since they represent missed opportunities that may never recur.

Emerging Technologies Reshaping Trend Detection

The field of multi-platform trend detection continues to evolve rapidly. Several emerging developments promise to reshape the landscape in the coming years.

Large Language Models and Semantic Understanding

Large language models offer unprecedented capabilities for understanding the semantic content of social media signals. Rather than relying on keyword matching or topic modelling, LLMs can interpret nuance, detect sarcasm, and understand context in ways that previous approaches could not.

Research from ArXiv on vision-language models notes that the emergence of these models offers exciting opportunities for advancing multi-sensor fusion, facilitating cross-modal understanding by incorporating semantic context into perception tasks. Future developments may focus on integrating these models with fusion frameworks to improve generalisation.

Knowledge Graph Integration

Knowledge graphs encode relationships and attributes between entities using graph structures. Research on future directions in data fusion notes that researchers are exploring algorithms based on the combination of knowledge graphs and graph attention models to combine information from different levels.

For trend detection, knowledge graphs can provide context about entities mentioned in social media, helping algorithms distinguish between different meanings of ambiguous terms and understand the relationships between topics.

Federated and Edge Computing

As trend detection moves toward real-time applications, the computational demands become severe. Federated learning and edge computing offer approaches to distribute this computation, enabling faster detection while preserving privacy.

Research on adaptive deep learning-based distributed Kalman Filters shows how these approaches dynamically adjust to changes in sensor reliability and network conditions, improving estimation accuracy in complex environments.

Adversarial Robustness

As trend detection systems become more consequential, they become targets for manipulation. Coordinated campaigns can generate artificial signals designed to trigger false positive detections, promoting content or ideas that would not otherwise trend organically.

Detecting and defending against such manipulation requires ongoing research into adversarial robustness. The same techniques used for detecting misinformation and coordinated inauthentic behaviour can be applied to filtering trend detection signals, ensuring that detected trends represent genuine organic interest rather than manufactured phenomena.

Synthesising Signals in an Uncertain World

The fusion of weak signals across multiple platforms to detect emerging trends is neither simple nor solved. It requires drawing on decades of research in signal processing, machine learning, and information retrieval. It demands careful attention to the trade-offs between recency and authority, between speed and accuracy, between catching genuine trends and avoiding false positives.

There is no universal answer to the question of acceptable false positive rates. A spam filter should aim for less than 1%. A fraud detection system may tolerate 25% or more. A marketing trend detector might accept 10%. The right threshold depends entirely on the costs and benefits in the specific application context.

Validation against ground truth is essential but challenging. Ground truth itself is difficult to establish for emerging trends, and standard metrics like AUC and F1 score cannot be simultaneously optimised. The most sophisticated systems combine algorithmic detection with human curation, using human judgement to interpret and validate what algorithms surface.

As the volume and velocity of social media data continue to grow, as new platforms emerge and existing ones evolve, the challenge of trend detection will only intensify. The algorithms and heuristics described here provide a foundation, but the field continues to advance. Those who master these techniques will gain crucial advantages in understanding what is happening now and anticipating what will happen next.

The signal is out there, buried in the noise. The question is whether your algorithms are sophisticated enough to find it.

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References and Sources

1. EURASIP Journal on Advances in Signal Processing. “Emerging trends in signal processing and machine learning for positioning, navigation and timing information: special issue editorial.” (2024). https://asp-eurasipjournals.springeropen.com/articles/10.1186/s13634-024-01182-8

2. VLDB Journal. “A survey of multimodal event detection based on data fusion.” (2024). https://link.springer.com/article/10.1007/s00778-024-00878-5

3. ScienceDirect. “Multi-sensor Data Fusion – an overview.” https://www.sciencedirect.com/topics/computer-science/multi-sensor-data-fusion

4. ArXiv. “A Gentle Approach to Multi-Sensor Fusion Data Using Linear Kalman Filter.” (2024). https://arxiv.org/abs/2407.13062

5. Wikipedia. “Dempster-Shafer theory.” https://en.wikipedia.org/wiki/Dempster–Shafer_theory

6. Nature Scientific Reports. “A new correlation belief function in Dempster-Shafer evidence theory and its application in classification.” (2023). https://www.nature.com/articles/s41598-023-34577-y

7. iMerit. “Managing Uncertainty in Multi-Sensor Fusion with Bayesian Methods.” https://imerit.net/resources/blog/managing-uncertainty-in-multi-sensor-fusion-bayesian-approaches-for-robust-object-detection-and-localization/

8. University of Cambridge. “Bayesian Approaches to Multi-Sensor Data Fusion.” https://www-sigproc.eng.cam.ac.uk/foswiki/pub/Main/OP205/mphil.pdf

9. Wikipedia. “Ensemble learning.” https://en.wikipedia.org/wiki/Ensemble_learning

10. Twitter Developer. “Trend Detection in Social Data.” https://developer.twitter.com/content/dam/developer-twitter/pdfs-and-files/Trend-Detection.pdf

11. ScienceDirect. “Twitter trends: A ranking algorithm analysis on real time data.” (2020). https://www.sciencedirect.com/science/article/abs/pii/S0957417420307673

12. Covert. “How AI Models Rank Conflicting Information: What Wins in a Tie?” https://www.covert.com.au/how-ai-models-rank-conflicting-information-what-wins-in-a-tie/

13. Wikipedia. “PageRank.” https://en.wikipedia.org/wiki/PageRank

14. Rutgers University. “Forward Decay: A Practical Time Decay Model for Streaming Systems.” https://dimacs.rutgers.edu/~graham/pubs/papers/fwddecay.pdf

15. ArXiv. “Solving Freshness in RAG: A Simple Recency Prior and the Limits of Heuristic Trend Detection.” (2025). https://arxiv.org/html/2509.19376

16. IBM. “What Is Ground Truth in Machine Learning?” https://www.ibm.com/think/topics/ground-truth

17. Google Developers. “Classification: Accuracy, recall, precision, and related metrics.” https://developers.google.com/machine-learning/crash-course/classification/accuracy-precision-recall

18. Virus Bulletin. “Measuring and marketing spam filter accuracy.” (2005). https://www.virusbulletin.com/virusbulletin/2005/11/measuring-and-marketing-spam-filter-accuracy/

19. Process Software. “Avoiding False Positives with Anti-Spam Solutions.” https://www.process.com/products/pmas/whitepapers/avoiding_false_positives.html

20. FraudNet. “False Positive Definition.” https://www.fraud.net/glossary/false-positive

21. Ravelin. “How to reduce false positives in fraud prevention.” https://www.ravelin.com/blog/reduce-false-positives-fraud

22. Columbia University. “False Discovery Rate.” https://www.publichealth.columbia.edu/research/population-health-methods/false-discovery-rate

23. Check Point Software. “What is a False Positive Rate in Cybersecurity?” https://www.checkpoint.com/cyber-hub/cyber-security/what-is-a-false-positive-rate-in-cybersecurity/

24. PMC. “Fake social media news and distorted campaign detection framework using sentiment analysis and machine learning.” (2024). https://pmc.ncbi.nlm.nih.gov/articles/PMC11382168/

25. EPJ Data Science. “Early detection of promoted campaigns on social media.” (2017). https://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-017-0111-y

26. ResearchGate. “Hot Topic Detection Based on a Refined TF-IDF Algorithm.” (2019). https://www.researchgate.net/publication/330771098_Hot_Topic_Detection_Based_on_a_Refined_TF-IDF_Algorithm

27. Quality and Reliability Engineering International. “Novel Calibration Strategy for Kalman Filter-Based Measurement Fusion Operation to Enhance Aging Monitoring.” https://onlinelibrary.wiley.com/doi/full/10.1002/qre.3789

28. ArXiv. “Integrating Multi-Modal Sensors: A Review of Fusion Techniques.” (2025). https://arxiv.org/pdf/2506.21885

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Tim Green UK-based Systems Theorist & Independent Technology Writer

Tim explores the intersections of artificial intelligence, decentralised cognition, and posthuman ethics. His work, published at smarterarticles.co.uk, challenges dominant narratives of technological progress while proposing interdisciplinary frameworks for collective intelligence and digital stewardship.

His writing has been featured on Ground News and shared by independent researchers across both academic and technological communities.

ORCID: 0009-0002-0156-9795 Email: tim@smarterarticles.co.uk

In Summary: * Having just taken the night meds, and the brain already slowing down, I can reliably predict a quiet evening ahead. Listening to relaxing music now, shall work on the night prayers, then start shutting things down around here.

Prayers, etc.: * daily prayers

Health Metrics: * bw= 220.90 lbs. * bp= 142/85 (66)

Exercise: * kegel pelvic floor exercises, half squats, calf raises, wall push-ups

Diet: * 07:10 – 1 peanut butter sandwich * 07:30 – 1 banana * 10:30 – plate of pancit * 12:45 – 1 fresh apple * 14:00 – 2 fried eggs, bacon, fried rice

Activities, Chores, etc.: * 07:00 – bank accounts activity monitored * 07:30 – read, pray, follow news reports from various sources, surf the socials, nap * 12:00 – watching an NFL Wild Card Playoff Game, Bills vs Jaguars * 15:05 – after the Bills won, turned off the TV and turned on the radio, tuned to B97 – The Home for IU Women's Basketball, broadcast from Bloomington, Indiana, for pregame coverage then for the radio call of tonight's NCAA Women's College Basketball Game, Indiana Hoosiers vs. Iowa Hawkeyes.... And Iowa wins 56 to 53. * 18:00 – read, pray, follow news reports from various sources, surf the socials

Chess: * 12:15 – moved in all pending CC games

As a freelancer, I write a lot of content for business owners—which is actually very helpful as a writer, because there are some parts of the process where you kind of want to think like an entrepreneur. And I think I’m in one of those stages now. I've had the concept of a novel running around in my brain for a while, but I've been struggling to get it going on the page. Don't get me wrong—I've written a lot of words for it. I've built more of the world than I probably need to, created a bunch of characters, and had a fair number of false starts on the plot. It’s just all of that hasn’t yet come together into a cohesive book.

But this year, I've resolved to finally get the first draft of it written. And I know I can. I've written novels before, and all of the pieces are there. I just need some new strategies to put them into place. So I went back through my notes from a couple of recent relevant freelance projects and picked out a few things I'm going to adapt to my novel writing to help me get this thing in motion.

#1: Set SMART goals.

This one's prevalent in academia as well as the corporate world, so it's something I'd imagine most readers have at least seen. Just in case, though, SMART stands for Specific, Measurable, Achievable, Relevant, and Time-Bound. In other words, to set effective goals, you want to make sure you clearly define exactly what you'll do and by when, and that this is something you can realistically achieve that will move you closer to your ultimate objective. 

Many of the writers I know are very bad at this. I'm including myself here. There's this temptation to say that creative projects need to happen “at the will of the muse” and you shouldn't limit them with metrics or deadlines. But “waiting for the muse” is also an easy way to write exactly nothing. Attatching actual numbers, dates, and other specific details to your goals makes them real, and you don't need to sacrifice any creativity to do so. 

SMART goals also don't need to be complicated. My big-picture goal for the novel is: “Finish the full first draft of the novel by the end of 2026.” This checks all 5 of boxes: it concerns a specific work and defines what stage of completion I want to have it in by a defined point in time. And I've written many book-length manuscripts within a year before, so I know this is achievable, especially since I have a solid chunk of the worldbuilding done. 

Of course, most business gurus would say it's best to set goals with smaller timelines than an entire year, which is why the second thing I'm adopting is...

#2: Break big goals into small milestones. 

Writing a speculative novel is a big project with a lot of moving parts, enough of them that trying to wrap your head around it can feel overwhelming if you try to think about it as one massive entity. That same thing happens to entrepreneurs when they're trying to start a business, or scale it, or optimize their systems, or whatever other business-ey things they need to do. 

The solution to this is to stop thinking of it as One Big Thing. Mark that down as your eventual end point, but then zoom in a bit and think through the various steps that you'll need to take to get there. Exactly what that looks like will depend on how you tend to write. If you're a plotter, the first step will be writing the outline, then those chapters you plot out can each be their own milestone. For pantsers, it might be more effective to break it down base on word count, setting milestones at every few thousand words. The point isn't so much how you subdivide the tasks, but just that you give yourself bite-sized chunks that don't feel as scary to tackle as a whole book. 

I've used this strategy to help motivate me through long projects before. I'm typically a pantser, so my usual approach is to set word count goals. With this book, though, I'm actually doing a bit of broad-strokes outlining and plot planning before trying to start the next round of writing. I haven't outlined down to the chapter-by-chapter level, but I've mapped out the major plot points, and am using those arrival points as my milestones. Which actually leads nicely into...

#3: Use the right tools for the project.

If you're even peripherally connected to the entrepreneurial world, you know there is a seemingly infinite array of websites, apps, SaaS platforms, and other tech available for small businesses. There are so many because each one focuses on a slightly different area. The ones for startups have different features than the ones for big enterprises; some are all-in-one solutions while others are just for accounting, or inventory tracking, or automating your marketing—you get the idea. Just because a program works well for one business doesn't mean it's the right choice for another one. And the best tool for a new company isn't necessarily what they'll use forever. As the company gets bigger, or changes its focus, the best tools for them to use are probably going to change, too. Smart companies regularly audit their tech stack to make sure it reflects their current needs and look for ways they could use it more efficiently.

The same thing goes for writing. It's smart to have some go-to tools, but you don't want to fall into the trap of using the same tools just out of habit, especially if they don't seem to be working like they used to. That was the realization I had after a few false starts on this project. Clearly, my usual approach of “find it when I write it” wasn't getting me where I needed to go. Maybe it would eventually, if I kept beating my head against that metaphorical wall, but it seems like I might get where I'm going faster if I try out some different ways of getting there. Hence the switch to writing an outline: it's a new tool I haven't tried yet, and one that might be a better fit for the story I'm trying to write. 

I'd say this goes for other types of tools, too. If you usually type in Micosoft Word but are feeling stuck, try handwriting for a bit, or use a different platform like Google Docs, or try cutting out the writing middle man entirely and speak your story into words using your phone's speech-to-text tool. At the worst, it's a way to come at the story from a fresh angle, and that can shake some new ideas loose. And, in the process, you might find a better tool for your writing process that you can work into your regular routine. 

#4: Know your market. 

My first impulse is to push back against this advice every time I hear it. My knee-jerk reaction is that “writing to a market” feels sales-ey and sell-out-ish. I want to tell the stories that excite me, not the things I think other people want to read. But I think this advice doesn't need to mean changing the story you're telling. Instead, I think it's about understanding who is most likely to read what you write, what other things they're reading, and how your story is going to fit in with the other fiction that's out there.

This isn't just something that comes up during the marketing stage, either. When you're stuck on a project, reading other stuff that's in the same area can help to spark ideas. I've been aiming to do this lately, curating my reading list to focus on novels that are in the same kind of genre-blurring, sci-f/fantasy territory. I'm also actively seeking out books that were published within the last year. It's a way for me to see what's going on in this corner of the publishing landscape right now, and also lets me get a head start on building my list of comps when I am done with the novel and ready to start querying. 

#5: Focus on the things that will have the biggest impact. 

Another must-do for any small business is to find its niche. Successful entrepreneurs know how to identify the specific value they offer to customers and devote most (if not all) of their energy to that most valuable product. On the other side, when businesses try to offer too many different products or services, or they try appeal to too broad of an audience, then they can end up sabotaging their efforts because their messaging is confusing, or they're trying to split their attention into too many directions. 

Writers make this mistake, too. Or at least I do. And I've definitely been suffering from low focus on this particular project. The world I'm working in is a very fun one, weaving in elements of folklore along with sci-fi and post-apocalyptic aspects—which means lots of opportunities to fall down research rabbit holes, on top of the temptation to go overboard building the world beyond the scope of what's actually needed for the story. 

And that ultimately needs to be the question guiding all of the effort I put toward the novel: What story am I telling, and what details does the reader need to enjoy that story to its fullest? Any world worth writing in is going to have far more interesting stuff in it than will fit in a single story. That's what gives it that feeling of a real place on the page and makes readers want to spend time there. But that can also turn into a trap, because it means there will always be new interesting corners of the world to discover if you let yourself keep wandering, and you can do that forever without ever actually producing a novel if you don't impose some limits on your imagination. 

I expect this will prove the most useful of the strategies I'm adopting for me to finally make progress on this novel, because I think this is the main roadblack that's been keeping me from moving forward. Of course, I haven't written the damn thing yet, so I suppose there's no proof at this point that they'll help me at all. But I'm excited to take a new approach to this book, and I feel like I have a clearer view of how to move forward than I did on my last attempt at the novel, so in that respect I suppose they've already given me a boost. 

See similar posts:

#NovelWriting #WritingAdvice

23 December 2025

Terminal advertisement (working title): a painting put into action today based on seeing that aforementioned Brazil tourism ad of Christ the Redeemer while on a moving walkway on my way through Heathrow. There's something emerging in the studio about the reconstruction of particular moments of seeing that I hope is beginning to stretch beyond the full stop stillness I have perhaps tried to capture in the past. And I think it has to do with identifying imagistic planes that somehow relate to the multiplicity of specific lived sensations. In the recall of the kinds of scenes I'm inclined to paint, I'm finding—through photos, sketches, and memory—that there becomes a kind of 360 degree inventory of phenomena that holds possible planar ingredients. And while I don't want to fall into the trap of manufacturing those ingredients, I do think they are worth noticing. In Flat window, they were represented by a combination of perceptions related to reflections, barriers, borderlines, and changes in light that became essentially a sequence of transparencies to layer on top of one another toward a hybrid image.

In this painting today, it seemed like the phenomena were less distinct and perhaps manifested more as a melding of planes rather than a separating and layering of them. I think I can trace this to the experience of seeing the advertisement itself: the micro shifts in fluorescent light bouncing off of the vinyl image as I passed it, the ambiguous tonal environment around it that seemed to blend into a big neutral goop, seeing the seams between each vinyl panel and then losing them again—those were the bits of recall that became planar and then united in shapelessness, the Christ figure a strangely warping and beckoning bit of solidity swimming in and around them.

21 December 2025

Devotional objects in my room at my new flat, a week since moving in:

My great grandfather's watch that my mother restored and gifted to me (Gruen, 1936), the white linen Ruba gifted to me (underneath the watch, forming a bed for it), the Korean celadon ceramic turtle Yena gifted to me, my yellow-orange telecaster that my father gifted to me, the antique Italian bronze candleholder (currently being used as an incense holder) that Yena gifted to me after her most recent trip to Venice, the house slippers that Yena gifted to me, the family photos (a mix of photos dating back to the early 90s comprised of Polaroids, photo booth photos, and prints of my mother's film photography) that I have mounted on the back of my door.