Accelerating dynamics of collective attention

Lorenz-Spreen, Philipp; Mønsted, Bjarke; Hövel, Philipp; Lehmann, Sune

doi:10.1038/s41467-019-09311-w

Cited by 192 publications

(153 citation statements)

References 30 publications

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“…Depending on the field, the stretched exponential decay factor ranges between 0 and 1 as shown by Figure 3 [48], putting emphasis on initial occurrences of an item without nullifying its later occurrences. Our results are also in line with previous studies which illustrate the universality of novelty decay in fields investigating social media and economics [21,26,42,57], which may be further investigated using neural network based models. Ultimately, our model strives to closely represent human attention mechanisms in machine learning tasks.…”

Section: Laptopsupporting

confidence: 91%

Incorporating Word Significance into Aspect-Level Sentiment Analysis

et al. 2019

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Aspect-level sentiment analysis has drawn growing attention in recent years, with higher performance achieved through the attention mechanism. Despite this, previous research does not consider some human psychological evidence relating to language interpretation. This results in attention being paid to less significant words especially when the aspect word is far from the relevant context word or when an important context word is found at the end of a long sentence. We design a novel model using word significance to direct attention towards the most significant words, with novelty decay and incremental interpretation factors working together as an alternative for position based models. The interpretation factor represents the maximization of the degree each new encountered word contributes to the sentiment polarity and a counter balancing stretched exponential novelty decay factor represents decaying human reaction as a sentence gets longer. Our findings support the hypothesis that the attention mechanism needs to be applied to the most significant words for sentiment interpretation and that novelty decay is applicable in aspect-level sentiment analysis with a decay factor β = 0.7 .

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Section: Laptopsupporting

confidence: 91%

Incorporating Word Significance into Aspect-Level Sentiment Analysis

et al. 2019

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“…The pace of online discussions and collective attention has accelerated over the recent years [9]. The study of algorithmic consensus facilitation and distributed decision making and control is both timely and relevant for our society.…”

Section: Discussionmentioning

confidence: 99%

Real-time Internet Control of Situated Human Agents

Pescetelli

Cebrián

Rahwan

2020

Preprint

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We present an online platform, called BeeMe, designed to test the current boundaries of Internet collective action and problem solving.BeeMe allows a scalable internet crowd of online users to collectively control the actions of a human surrogate acting in physical space.BeeMe demonstrates how intelligent goal-oriented decision-making can emerge from large crowds in quasi real-time. We analyzed data collected from a global BeeMe live performance that involved thousands of individuals, collectively solving a sci-fi Internet mystery. We study simple heuristic algorithms that read in users' chat messages and output human actionable commands representing majority preferences, and compare their performance to the behavior of a human operator solving the same task. Results show that simple heuristics can achieve near-human performance in interpreting the democratic consensus. When human and machine's output differ, the discrepancy is often due to human bias favoring non-representative views. We discuss our results in light of previous work and the contemporary debate on democratic digital systems.Humans are extreme cooperators [18,20]. The evolution of language allowed our species to coordinate actions, thoughts and intentions. Contrary to other social species -like eusocial insects and other primates -humans cooperate in large-scale societies of largely non-related individuals [4,6,8]. The increasing returns of group scale, evident for instance in warfare, big game hunting, or tax collection, has put pressure on humans to develop norms and institutions that allow increasingly larger social groups to cooperate and act together [3,21,22]. Social norms and institutionsdefined as the set of culturally acquired rules governing the behavior of individuals in specific contexts [21]-are considered solutions to the collective action problem, arising when selfish instincts must be suppressed in favor of higher group payoffs [8,13,17]. Religion and political states are thought to have provided a range of innovations-like moralizing gods, policing and representative hierarchies -that maintain fairness, punish non-cooperators and increase

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“…Fierce competition for human attention has led to the growing fragmentation of collective attention, with ever greater proliferation of novelty-driven content and shorter attention intervals allocated to particular topics (Lorenz-Spreen, Mønsted, Hövel, & Lehmann, 2019).…”

Section: Distractive Environmentsmentioning

confidence: 99%

Citizens Versus the Internet: Confronting Digital Challenges With Cognitive Tools

Kozyreva¹,

Lewandowsky²,

Hertwig³

2019

Preprint

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The Internet has evolved into a ubiquitous and indispensable digital environment in which people communicate, seek information, and make decisions. Despite offering various benefits, online environments are also replete with smart, highly adaptive choice architectures designed primarily to maximize commercial interests, capture and sustain users’ attention, monetize user data, and predict and influence future behavior. This online landscape holds multiple negative consequences for society, such as a decline in human autonomy, rising incivility in online conversation, the facilitation of political extremism, and the spread of disinformation. Benevolent choice architects working with regulators may curb the worst excesses of manipulative choice architectures, yet the strategic advantages, resources, and data remain with commercial players. One way to address this imbalance is with interventions that empower Internet users to gain some control over their digital environments, in part by boosting their information literacy and their cognitive resistance to manipulation. Our goal is to present a conceptual map of interventions that are based on insights from psychological science. We begin by systematically outlining how online and offline environments differ despite being increasingly inextricable. We then identify four major types of challenges that users encounter in online environments: persuasive and manipulative choice architectures, AI-assisted information architectures, false and misleading information, and distractive environments. Next, we turn to how psychological science can inform interventions to counteract these challenges of the digital world. After distinguishing between three types of behavioral and cognitive interventions—nudges, technocognition, and boosts—we focus in on boosts, of which we identify two main groups: (1) those aimed at enhancing people’s agency in their digital environments (e.g., self-nudging, deliberate ignorance) and (2) those aimed at boosting competences of reasoning and resilience to manipulation (e.g., simple decision aids, inoculation). These cognitive tools are designed to foster the civility of online discourse and protect reason and human autonomy against manipulative choice architectures, attention-grabbing techniques, and the spread of false information.

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Accelerating dynamics of collective attention

Cited by 192 publications

References 30 publications

Incorporating Word Significance into Aspect-Level Sentiment Analysis

Incorporating Word Significance into Aspect-Level Sentiment Analysis

Real-time Internet Control of Situated Human Agents

Citizens Versus the Internet: Confronting Digital Challenges With Cognitive Tools

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