ABSTRACT

Short videos on social media are the dominant way young people consume content. News outlets aim to reach audiences through news reels—short videos conveying news—but struggle to translate traditional journalistic formats into short, entertaining videos. To translate news into social media reels, we support journalists in reframing the narrative. In literature, narrative framing is a high-level structure that shapes the overall presentation of a story. We identified three narrative framings for reels that adapt social media norms but preserve news value, each with a different balance of information and entertainment. We introduce ReelFramer, a human-AI co-creative system that helps journalists translate print articles into scripts and storyboards. ReelFramer supports exploring multiple narrative framings to find one appropriate to the story. AI suggests foundational narrative details, including characters, plot, setting, and key information. ReelFramer also supports visual framing; AI suggests character and visual detail designs before generating a full storyboard. Our studies show that narrative framing introduces the necessary diversity to translate various articles into reels, and establishing foundational details helps generate scripts that are more relevant and coherent. We also discuss the benefits of using narrative framing and foundational details in content retargeting.

We examine how social media plays the role of an attention driver for traditional media. Social media attracts and channels attention to a topic. This attention triggers people to seek further information that is reported professionally in traditional media. Specifically, the volume of social media posts about a stock influences the attention to this stock the next day, proxied by the viewership of news articles on the same stock published the next day. We test this hypothesis in the stock market context because social media is less likely than traditional media to diffuse fundamental information in the stock market. Analysing stock-related news articles and stock-related social media posts from Sina Finance and Sina Weibo, we find that the social media post volume of a stock at time t-1 is associated with the traditional media viewership of the same stock at time t. This effect is amplified when social media sentiment about the stock is more intense or positive, and with an increase in the volume of verified social media posts about the stock. Our results provide evidence that social media platforms act as attention drivers, which differ from the information channel functions discussed in prior literature.

News media often leverage documents to find ideas for stories, while being critical of the frames and narratives present. Developing angles from a document such as a press release is a cognitively taxing process, in which journalists critically examine the implicit meaning of its claims. Informed by interviews with journalists, we developed AngleKindling, an interactive tool which employs the common sense reasoning of large language models to help journalists explore angles for reporting on a press release. In a study with 12 professional journalists, we show that participants found AngleKindling significantly more helpful and less mentally demanding to use for brainstorming ideas, compared to a prior journalistic angle ideation tool. AngleKindling helped journalists deeply engage with the press release and recognize angles that were useful for multiple types of stories. From our findings, we discuss how to help journalists customize and identify promising angles, and extending AngleKindling to other knowledge-work domains.

Abstract Mass media sentiment of financial news significantly influences investment decisions of investors. Hence, studying how this sentiment emerges is important. In years past, this was straightforward, often dictated by journalists who cover financial news, but this has become more complex now. In this paper, we focus on how social media sentiment affects mass media sentiment. Using data from Sina Weibo and Sina Finance (around 60 million weibos and 6.2 million news articles), we show that social media does influence mass media sentiment emergence for financial news. The sentiment consistency between social media reaction and prior news articles amplifies the persistence of mass media sentiment over time. By contrast, we found limited evidence of social media reducing the persistence of mass media sentiment over time. The results have significant implications for understanding how 2 types of media, treated separately in the literature, may be connected.

Online reviews influence consumers’ purchase decisions. Product type – specifically, whether a product is utilitarian or hedonic – can help explain how consumers react to reviews. Characteristics of reviews – in particular, valence, measured by ratings, the arousal level of the language used in the text and the volume of the reviews – provide heuristics that consumers may use in making purchase decisions. Product type moderates the effect of these characteristics. Empirical evidence for this claim comes from multiple sources: a panel data analysis of 26,357 Amazon products and an online experiment with 541 participants. The findings of studies based on this evidence show that product type (hedonic or utilitarian) moderates the effect of the three heuristic attributes of online reviews (valence, volume, and arousal) on sales. The analysis uses OLS-fixed effects models and Granger causality tests. These findings explain why past studies have found that sometimes online review valence is more influential than volume and arousal with respect to sales and why sometimes this is reversed. Our findings have significant theoretical and practical implications for the design of choice architectures in online review systems.

Bots are playing an increasingly important role in the creation of knowledge in Wikipedia. In many cases, editors and bots form tightly knit teams. Humans develop bots, argue for their approval, and maintain them, performing tasks such as monitoring activity, merging similar bots, splitting complex bots, and turning off malfunctioning bots. Yet this is not the entire picture. Bots are designed to perform certain functions and can acquire new functionality over time. They play particular roles in the editing process. Understanding these roles is an important step towards understanding the ecosystem, and designing better bots and interfaces between bots and humans. This is important for understanding Wikipedia along with other kinds of work in which autonomous machines affect tasks performed by humans. In this study, we use unsupervised learning to build a nine category taxonomy of bots based on their functions in English Wikipedia. We then build a multi-class classifier to classify 1,601 bots based on labeled data. We discuss different bot activities, including their edit frequency, their working spaces, and their software evolution. We use a model to investigate how bots playing certain roles will have differential effects on human editors. In particular, we build on previous research on newcomers by studying the relationship between the roles bots play, the interactions they have with newcomers, and the ensuing survival rate of the newcomers.

This all-day workshop aims to promote convergence among its participants on research related to working with intelligent machines. We define intelligent machines as both material (e.g., robots) and immaterial (e.g., algorithms) computing technologies that can be characterized by autonomy, the ability to learn, and the ability to interact with other systems and with humans. The workshop has three goals: identifying specific research problems around work and intelligent machines, developing a common language base that can facilitate interdisciplinary collaboration among researchers, and identifying information and cyber-infrastructure needs to support convergent research. Workshop activities will facilitate interdisciplinary dialogue and strive to generate high-impact research ideas to advance each of these goals.

A key issue, whenever people work together to solve a complex problem, is how to divide the problem into parts done by different people and combine the parts into a solution for the whole problem. This paper presents a novel way of doing this with groups of contests called contest webs. Based on the analogy of supply chains for physical products, the method provides incentives for people to (a) reuse work done by themselves and others, (b) simultaneously explore multiple ways of combining interchangeable parts, and (c) work on parts of the problem where they can contribute the most.

Crowds can generate ideas by searching for new designs. A model for such crowd-based search is proposed consisting of three major forces: the problem domain, the actors, and the process. One particular process that can perform such search is that described by human based genetic algorithms, in which crowds are responsible for creating, modifying, and combining designs. This study looks at one aspect of the process: the alternative generation algorithm. Three systems were built that performed greenfield, modification and combination-based alternative generation. These were compared in an experiment involving 2220 participants who played different roles in creating and evaluating advertisements. The results favor the modification system. This suggests for domains like advertising, crowd-based design systems should encourage a series of modifications of initial ideas. For designers of other crowd-based systems in other problem domains, this study suggests that both modification and combination processes should be tested and their ratio of use adjusted according to the results obtained, much as the ratio of mutation and crossover are adjusted in genetic algorithms.

Paid crowd work offers remarkable opportunities for improving productivity, social mobility, and the global economy by engaging a geographically distributed workforce to complete complex tasks on demand and at scale. But it is also possible that crowd work will fail to achieve its potential, focusing on assembly-line piecework. Can we foresee a future crowd workplace in which we would want our children to participate? This paper frames the major challenges that stand in the way of this goal. Drawing on theory from organizational behavior and distributed computing, as well as direct feedback from workers, we outline a framework that will enable crowd work that is complex, collaborative, and sustainable. The framework lays out research challenges in twelve major areas: workflow, task assignment, hierarchy, real-time response, synchronous collaboration, quality control, crowds guiding AIs, AIs guiding crowds, platforms, job design, reputation, and motivation.

A method of organizing the crowd to generate ideas is described. It integrates crowds using evolutionary algorithms. The method increases the creativity of ideas across generations, and it works better than greenfield idea generation. Specifically, a design space of internet-scale idea generation systems is defined, and one instance is tested: a crowd idea generation system that uses combination to improve previous designs. The key process of the system is the following: A crowd generates designs, then another crowd combines the designs of the previous crowd. In an experiment with 540 participants, the combined designs were compared to the initial designs and to the designs produced by a greenfield idea generation system. The results show that the sequential combination system produced more creative ideas in the last generation and outperformed the greenfield idea generation system. The design space of crowdsourced idea generation developed here may be used to instantiate systems that can be applied to a wide range of design problems. The work has both pragmatic and theoretical implications: New forms of coordination are now possible, and, using the crowd, it is possible to test existing and emerging theories of coordination and participatory design. Moreover, it may be possible for human designers, organized as a crowd, to codesign with each other and with automated algorithms.

This study considers a situation in which a sensor network aims to protect a stationary target (e.g., a large battleship at anchor) and detects signals from approaching objects (e.g., small boats traveling in a harbor). Once the network detects a sufficient number of signals from the object (for example, through video surveillance), it may classify the object's intention as hostile and act accordingly by informing responders. The objective is to design an optimal strategy for recognition that guarantees accurate and timely intruder detection. We show that the optimal policy is of a control limit threshold.

Combining concept maps aids creative problem solving. In two studies, participants were presented with a story describing a problem and its solution. They were then asked to read an analogous problem and provide a solution. In order to facilitate transfer from the first story to the second, concept maps of the stories were elicited and participants instructed to combine them. The results show that participants were more likely to solve the problem when they produced and combined concept maps, as compared to a condition in which concept maps were used without combination, and compared to a condition in which no maps were used, but instead summaries were created and combined.

A sketch combination system is introduced and tested: a crowd of 1047 participated in an iterative process of design, evaluation and combination. Specifically, participants in a crowdsourcing marketplace sketched chairs for children. One crowd created a first generation of chairs, and then successive crowds created new generations by combining the chairs made by previous crowds. Other participants evaluated the chairs. The crowd judged the chairs from the third generation more creative than those from the first generation. An analysis of the design evolution shows that participants inherited and modified presented features, and also added new features. These findings suggest that crowd based design processes may be effective, and point the way toward computer-human interactions that might further encourage crowd creativity.

Target detection, identification, and tracking are tasks of great importance in counterterrorism preparations. The development of a new generation of mobile autonomous wireless robotic sensor agents provides new advanced opportunities for target monitoring and tracking. We are particularly interested in the investigation of the underwater motion of the mobile robotic sensor agents. However, the trajectory of the underwater motion of a robotic sensor agent from the initial point to the destination significantly differs from the ground motion trajectory. Firstly, an underwater vehicle is affected by the current flow and its motion strongly depends on the structure of the river (cross-section shape, bottom type, etc) or ocean. Secondly, the battery power is limited and it almost impossible to recharge the batteries while the agent is underwater. Also, the time for robotic sensor agents to reach the target may be crucial. Consequently, finding the fastest path to the destination given a water stream is important. Thus, how should cluster of underwater robotic sensors move to optimize multi-attribute tasks?

Probabilities of physical attack are often determined by various environmental factors. As the environment changes, the probability of attack associated with an area changes. In such dynamic environments, autonomous sensors are potentially useful to optimally cover regions that have high probabilities of attack. We present results from agent-based simulations, in which autonomous sensors "forage" a space to find areas with high attack probabilities. Simple heuristics often resulted in optimal coverage of the attack regions, without a centralized control. By varying how quickly sensors respond to a threat, we can encourage some sensors to cover some areas, and others to hang back and defend different areas, allowing them to distribute optimally as a team. The idea of making team members hang back may seem counterintuitive. In fact, people often converge all at once to respond to an immediate threat. Our results show that it is useful to have some agents remain behind, in case the environment changes.

The paper focuses on the design of remote sensing and detecting systems for the underwater environment around estuary areas. The behavior of estuary water conditions is studied and the environmental impacts on the performance of underwater sensing and detecting systems are investigated. Sensor coverage variations are determined in particular. Environmental data is extracted from the NYHOPS ocean model with periodic calibration from fixed CTD stations in the Hudson River. The result shows that careful planning is required for sensor placement in an estuary area.

Laboratories play a crucial role in the education of future scientists and engineers, yet there is disagreement among science and engineering educators about whether and which types of technology-enabled labs should be used. This debate could be advanced by large-scale randomized studies addressing the critical issue of whether remotely operated or simulation-based labs are as effective as the traditional hands-on lab format. The present article describes the results of a large-scale (N = 306) study comparing learning outcomes and student preferences for several different lab formats in an undergraduate engineering course. The lab formats that were evaluated included traditional hands-on labs, remotely operated labs, and simulations. Learning outcomes were assessed by a test of the specific concepts taught in each lab. These knowledge scores were as high or higher (depending on topic) after performing remote and simulated laboratories versus performing hands-on laboratories. In their responses to survey items, many students saw advantages to technology-enabled lab formats in terms of such attributes as convenience and reliability, but still expressed preference for hands-on labs. Also, differences in lab formats led to changes in group functions across the plan-experiment-analyze process: For example, students did less face-to-face work when engaged in remote or simulated laboratories, as opposed to hands-on laboratories.

Human category learning has been modeled using exemplar, prototype, and rule-based theories. Rule-based models are the least discussed. This paper presents a rule-based model based on evolutionary computation techniques. Such techniques allow for the combination of concepts, an important aspect of human cognition that has been largely overlooked in previous cognitive modeling research. We also include other human-like characteristic in the model, namely a simplicity bias and instance-based learning. The results suggest that such an algorithm can replicate well-known results in human category learning. We discuss the broader issue of which of the three models of categorization make sense in particular situations.

The limited power, low radio range, and an ever changing environment make the ability to explicitly communicate between multi-robots decreases in a searching task. When this happens, maintaining the weakened connection will cause robots to cluster during searching, which may be suboptimal with respect to the searching time. In this paper, several integration strategies are proposed to coordinate a team of robots which have limited explicit communication. To speed up the reconnection procedure for the proposed aggregate strategies, implicit communication through vision sensors is proposed in this paper to establish a movement plan to recover the explicit communication. Simulation results are presented and discussed; The real-world experiments with 3 Pioneer robots have been conducted. The proposed strategies can be extended to a large-scale searching environment as well as a combination of humans and robots

Wireless networks combined with location technology create new problems and call for new decision aids. As a precursor to the development of these decision aids, a concept of communication distance is developed and applied to six situations. This concept allows travel time and bandwidth to be combined in a single measure so that many problems can be mapped onto a weighted graph and solved through shortest path algorithms. The paper looks at the problem of intercepting an out-of-communication team member and describes ways of using planning to reduce communication distance in anticipation of a break in connection. The concept is also applied to ad hoc radio networks. A way of performing route planning using a bandwidth map is developed and analyzed. The general implications of the work to transportation planning are discussed.

A procedure for filter design is described for enhancing fingerprint images. Four steps of this procedure are described: user specification of appropriate image features, determination of local ridge orientations throughout the image, smoothing of this orientation image, and pixel-by-pixel image enhancement by application of oriented, matched filter masks. The contribution of this work is to quantify and justify the functional relationships between image features and filter parameters so that the design process can be easily modified for different conditions of noise and scale. Application of the filter shows good ridge separation, continuity, and background noise reduction.>