Quantitative approaches for evaluating the influence of films using the IMDb database

[EN] Why do films certain remain influential throughout film history? The purpose of this paper is to attempt to answer this question. To do so, we adopt some quantitative approaches that facilitate an objective interpretation of the data. The data source we have chosen for this study is the Internet Online Movie Database (IMDb), and in particular, one of its sections called "Connections", which lists references made to a film in subsequent movies and references made in the film itself to previous ones. The extraction and analysis of these networks of citations allows us to draw some conclusions about the most influential movies in film history, identifying their distinguishing features, and considering how their popularity has evolved over time.This work is part of the Project "Active Audiences and Journalism. Interactivity, Web Integration and Findability of Journalistic Information". CSO2012-39518-C04-02. National Plan for R+D+i, Spanish Ministry of Economy and CompetitivenessCanet Centellas, FJ.; Valero Navarro, MA.; Codina Bonilla, L. (2016). Quantitative approaches for evaluating the influence of films using the IMDb database. Communication & Society. 29(2):151-172. https://doi.org/10.15581/003.29.2.151-172S15117229

Quantitative meta-analysis of visual motifs throughout film history

This paper assesses the importance of visual motifs as new research tools for film and media studies, by running a quantitative meta-study of two expert review databases: the British Film Institute best-movies 2012 poll and the Encyclopedia of visual motifs in cinema (a joint venture of up to sixty researchers and film critics). The former is used as an indicator of canonic values in film historiography, while the latter represents a new approach to cinema from a less hierarchical and nonauteur- oriented methodology. Three main variables are quantified and compared (decades, genres, directors), pointing to a low accordance between the top films of both databases, as well as to noteworthy differences in terms of historical scope, high / low culture values, and authorship

Predicting Research Trends with Semantic and Neural Networks with an application in Quantum Physics

The vast and growing number of publications in all disciplines of science cannot be comprehended by a single human researcher. As a consequence, researchers have to specialize in narrow sub-disciplines, which makes it challenging to uncover scientific connections beyond the own field of research. Thus access to structured knowledge from a large corpus of publications could help pushing the frontiers of science. Here we demonstrate a method to build a semantic network from published scientific literature, which we call SemNet. We use SemNet to predict future trends in research and to inspire new, personalized and surprising seeds of ideas in science. We apply it in the discipline of quantum physics, which has seen an unprecedented growth of activity in recent years. In SemNet, scientific knowledge is represented as an evolving network using the content of 750,000 scientific papers published since 1919. The nodes of the network correspond to physical concepts, and links between two nodes are drawn when two physical concepts are concurrently studied in research articles. We identify influential and prize-winning research topics from the past inside SemNet thus confirm that it stores useful semantic knowledge. We train a deep neural network using states of SemNet of the past, to predict future developments in quantum physics research, and confirm high quality predictions using historic data. With the neural network and theoretical network tools we are able to suggest new, personalized, out-of-the-box ideas, by identifying pairs of concepts which have unique and extremal semantic network properties. Finally, we consider possible future developments and implications of our findings.Comment: 9+6 pages, 6 figure

Biometric data sharing in the wild:investigating the effects on online sports spectators

There has been a market surge in both provision of and demand for fitness applications and sport wearables. These werables often come equipped with highly sophisticated biometric data (e.g. heart rate) functionalities that make the capture and sharing of such biometric data increasingly common practice. A few research studies have considered the effect that sharing biometric data has on those individuals sharing this data. However, little is known regarding the social impact of sharing this data in real-time and online. In this study, we investigate whether there is value in sharing heart rate data within social applications and whether sharing this data influences the behavior of those seeing this data. We do so by conducting a study where the heart rate data of runners competing in a 5-km road race is shared in real-time with 140 online spectators. We collect rich quantitative data of user interaction though server logs, and a qualitative data set through interviews and online users' comments. We then compare and contrast the behavior of online spectators who are presented with heart rate data together with contextual data, and those who are only presented with contextual data, for example, location. We also examine whether this difference is dependent on the social relation between the athletes and the spectators. Results indicate that spectators who are presented with the runners' heart rate data support the athletes more and rate the presented system more positively. These effects are dependent on the social tie between the athletes and spectators. This is one of the first studies to carry out an empirical investigation in the wild on the effects of sharing heart rate data in an online social context. In this light, in addition to supporting earlier literature, the outcomes present new insights and research directions within the sporting context

A Corpus Linguistic Analysis of Domestic Violence in Law and Cinema

Domestic violence (DV) is pervasive worldwide and has negative psychological effects that last a lifetime. Although DV is a problem that affects all societies, it is addressed through varied legal responses and with gaps in comprehension when examined through a cultural lens. The findings of these studies have the potential to assist helping professionals in two ways: (a) providing a greater understanding of the language and cultural perception of DV to guide the development of successful interventions for victims and survivors, and (b) determining recommendations for targeted legal advocacy to address DV more effectively. The objective of this dissertation research project was twofold. First, it uses a corpus linguistic design to analyze the keyness and collocation of laws in the United States for the purpose of assessing the relationship between linguistic differences in law and differing rates of DV. The frequency of words was examined, as was the phrasing surrounding the key terms "child*," "juvenile*," and "minors" in the laws of the 10 states with the highest and lowest rates of DV using the software tools Antconc and #Lancsbox. Results indicated that the statutory state law, of the states with the highest and lowest rates of DV, differ significantly from one another. While the legislation in states with lower rates had broader definitions of DV and contained supportive measures for victims, the laws in states with higher rates tended to concentrate largely on physical acts of abuse. Second, using the software Sketch Engine, a diachronic linguistic study was conducted to evaluate single and multiword keywords to examine the evolution of language. An English speaking, DV themed film corpus was created, and its results were compared with those of a broader movie corpus. The research revealed that, in comparison to the general corpus, the language used in DV movies most conveyed sex, violence, dominance, and mental health symptomology. Also, there were sharp increases and decreases in the usage of specific words over time. Helping professionals can use the findings of these two research studies to understand the experiences of victims and offenders and to advocate for changes to laws and policies related to DV

Quality TV in the Streaming Age

The purpose of this Honors Thesis is to explore how viewers define quality TV for themselves. This study places the quality TV discourse within the context of the rise of the streaming industry and the decline in traditional moviegoing. To carry out this study, a Qualtrics survey with closed-ended questions and open-ended contextual questions was administered to 162 students at Bryant University. The survey sheds light on the characteristics that viewers deem most important in assessing the quality of television series while at the same time identifying recent shows that viewers believe deserve the quality TV label

Citizen Science: Reducing Risk and Building Resilience to Natural Hazards

Natural hazards are becoming increasingly frequent within the context of climate change—making reducing risk and building resilience against these hazards more crucial than ever. An emerging shift has been noted from broad-scale, top-down risk and resilience assessments toward more participatory, community-based, bottom-up approaches. Arguably, non-scientist local stakeholders have always played an important role in risk knowledge management and resilience building. Rapidly developing information and communication technologies such as the Internet, smartphones, and social media have already demonstrated their sizeable potential to make knowledge creation more multidirectional, decentralized, diverse, and inclusive (Paul et al., 2018). Combined with technologies for robust and low-cost sensor networks, various citizen science approaches have emerged recently (e.g., Haklay, 2012; Paul et al., 2018) as a promising direction in the provision of extensive, real-time information for risk management (as well as improving data provision in data-scarce regions). It can serve as a means of educating and empowering communities and stakeholders that are bypassed by more traditional knowledge generation processes. This Research Topic compiles 13 contributions that interrogate the manifold ways in which citizen science has been interpreted to reduce risk against hazards that are (i) water-related (i.e., floods, hurricanes, drought, landslides); (ii) deep-earth-related (i.e., earthquakes and volcanoes); and (iii) responding to global environmental change such as sea-level rise. We have sought to analyse the particular failures and successes of natural hazards-related citizen science projects: the objective is to obtain a clearer understanding of “best practice” in a citizen science context