Language Processing Methodologies Research Articles

Identifying patterns and mechanisms of AI integration in blockchain for e-voting network security

The study focuses on the enhancement of e-voting blockchain network security through the integration of artificial intelligence. The critical problem addressed is the existing limitations in real-time threat detection and anomaly detection within blockchain transactions. These limitations can compromise the integrity and security of blockchain networks, making them vulnerable to attacks and fraudulent activities. The core results of the research include the development and implementation of sophisticated AI algorithms designed to enhance the monitoring of blockchain transactions and the auditing of smart contracts. These AI-driven advancements introduce unique features, such as the capability to detect and respond to security threats and anomalies in real-time. This significantly strengthens and optimizes the security frameworks of blockchain systems in e-voting. These results are explained by the strategic application of machine learning and natural language processing methodologies. By employing these advanced AI techniques, the study has achieved more accurate and efficient threat detection, thereby addressing the security challenges previously mentioned. The practical applications of these findings are extensive and diverse. Enhanced security mechanisms can be utilized in financial transactions, supply chain management, and decentralized applications, providing a robust framework for improved blockchain-based e-voting security. In conclusion, integrating AI into blockchain security mechanisms addresses current limitations in threat detection and offers a scalable and effective solution for future security challenges

The impact of alcohol on affiliative verbal behavior: Asystematic review and meta-analysis.

Language is a fundamental aspect of human social behavior that is linked to many rewarding social experiences, such as social bonding. Potential effects of alcohol on affiliative language may therefore be an essential feature of alcohol reward and may elucidate pathways through which alcohol is linked to social facilitation. Examinations of alcohol's impact on language content, however, are sparse. Accordingly, this investigation represents the first systematic review and meta-analysis of alcohol's effects on affiliative language. We test the hypothesis that alcohol increases affiliative verbal approach behaviors and discuss future research directions. PsycInfo and Web of Science were systematically searched in March 2023 according to our preregistered plan. Eligible studies included social alcohol administration experiments in which affiliative verbal language was assessed. We present a random-effects meta-analysis that examines the effect of alcohol compared to control on measures of affiliative verbal behavior. Our search identified 16 distinct investigations (comprising 961 participants) that examined the effect of alcohol on affiliative verbal behavior. Studies varied greatly in methods and measures. Meta-analytic results demonstrated that alcohol is modestly associated with increases in affiliative verbal behavior (Hedges' g = 0.164, 95% CI [0.027, 0.301], p = 0.019). Study quality was rated using an adapted version of the Quality Assessment Tool for Quantitative Studies and did not significantly moderate alcohol's effects. This study provides preliminary evidence that alcohol can increase affiliative verbal behaviors. This effect may be an important feature of alcohol reward. Given heterogeneity in study features, low study quality ratings, and limited reporting of effect size data, results simultaneously highlight the promise of this research area and the need for more work. Advances in language processing methodologies that could allow future work to systematically expand upon this finding are discussed.

SALSA: Semantically-Aware Latent Space Autoencoder

In deep learning for drug discovery, molecular representations are often based on sequences, known as SMILES, which allow for straightforward implementation of natural language processing methodologies, one being the sequence-to-sequence autoencoder. However, we observe that training an autoencoder solely on SMILES is insufficient to learn molecular representations that are semantically meaningful, where semantics are specified by the structural (graph-to-graph) similarities between molecules. We demonstrate by example that SMILES-based autoencoders may map structurally similar molecules to distant codes, resulting in an incoherent latent space that does not necessarily respect the semantic similarities between molecules. To address this shortcoming we propose Semantically-Aware Latent Space Autoencoder (SALSA) for molecular representations: a SMILES-based transformer autoencoder modified with a contrastive task aimed at learning graph-to-graph similarities between molecules. To accomplish this, we develop a novel dataset comprised of sets of structurally similar molecules and opt for a supervised contrastive loss that is able to incorporate full sets of positive samples. We evaluate semantic awareness of SALSA representations by comparing to its ablated counterparts, and show empirically that SALSA learns representations that maintain 1) structural awareness, 2) physicochemical awareness, 3) biological awareness, and 4) semantic continuity.

The importance of online customer reviews characteristics on remanufactured product sales: Evidence from the mobile phone market on Amazon.com

Remanufactured products have emerged as an important option for achieving sustainable development, which is crucial to the success of the circular economy. Consequently, it is necessary to investigate the influence of online customer reviews on remanufactured product sales, an aspect that has not been well understood in current research. In this study, we employed natural language processing methodologies, analyzing over 50,000 reviews from 2020 to 2021 on Amazon.com to explore the impacts of online customer reviews on remanufactured product sales. The findings indicate that the formal characteristics of online customer reviews, that is, review length and review valence have positive influences on remanufactured product sales. Furthermore, the content characteristics of online customer reviews, including the volume and valence of product usability, product service quality, and product cost performance, positively promote remanufactured product sales. Additionally, this study reveals three emotional characteristics of online customer reviews that significantly influence remanufactured product sales. Specifically, contentment and surprise positively affect remanufactured product sales, while anger negatively affects remanufactured product sales. The findings of this study provide useful insights for remanufactured product sellers to manage online customer reviews and develop marketing strategies.

Incivility in COVID-19 Vaccine Mandate Discourse and Moral Foundations: Natural Language Processing Approach.

Vaccine hesitancy poses a substantial threat to efforts to mitigate the harmful effects of the COVID-19 pandemic. To combat vaccine hesitancy, officials in the United States issued vaccine mandates, which were met with strong antivaccine discourse on social media platforms such as Reddit. The politicized and polarized nature of COVID-19 on social media has fueled uncivil discourse related to vaccine mandates, which is known to decrease confidence in COVID-19 vaccines. This study examines the moral foundations underlying uncivil COVID-19 vaccine discourse. Moral foundations theory poses that individuals make decisions to express approval or disapproval (ie, uncivil discourse) based on innate moral values. We examine whether moral foundations are associated with dimensions of incivility. Further, we explore whether there are any differences in the presence of incivility between the r/coronaviruscirclejerk and r/lockdownskepticism subreddits. Natural language processing methodologies were leveraged to analyze the moral foundations underlying uncivil discourse in 2 prominent antivaccine subreddits, r/coronaviruscirclejerk and r/lockdownskepticism. All posts and comments from both of the subreddits were collected since their inception in March 2022. This was followed by filtering the data set for key terms associated with the COVID-19 vaccine (eg, "vaccinate" and "Pfizer") and mandates (eg, "forced" and "mandating"). These key terms were selected based on a review of existing literature and because of their salience in both of the subreddits. A 10% sample of the filtered key terms was used for the final analysis. Findings suggested that moral foundations play a role in the psychological processes underlying uncivil vaccine mandate discourse. Specifically, we found substantial associations between all moral foundations (ie, care and harm, fairness and cheating, loyalty and betrayal, authority and subversion, and sanctity and degradation) and dimensions of incivility (ie, toxicity, insults, profanity, threat, and identity attack) except for the authority foundation. We also found statistically significant differences between r/coronaviruscirclejerk and r/lockdownskepticism for the presence of the dimensions of incivility. Specifically, the mean of identity attack, insult, toxicity, profanity, and threat in the r/lockdownskepticism subreddit was significantly lower than that in the r/coronaviruscirclejerk subreddit (P<.001). This study shows that moral foundations may play a substantial role in the presence of incivility in vaccine discourse. On the basis of the findings of the study, public health practitioners should tailor messaging by addressing the moral values underlying the concerns people may have about vaccines, which could manifest as uncivil discourse. Another way to tailor public health messaging could be to direct it to parts of social media platforms with increased uncivil discourse. By integrating moral foundations, public health messaging may increase compliance and promote civil discourse surrounding COVID-19.

Utilizing an Attention-Based LSTM Model for Detecting Sarcasm and Irony in Social Media

Sarcasm and irony represent intricate linguistic forms in social media communication, demanding nuanced comprehension of context and tone. In this study, we propose an advanced natural language processing methodology utilizing long short-term memory with an attention mechanism (LSTM-AM) to achieve an impressive accuracy of 99.86% in detecting and interpreting sarcasm and irony within social media text. Our approach involves innovating novel deep learning models adept at capturing subtle cues, contextual dependencies, and sentiment shifts inherent in sarcastic or ironic statements. Furthermore, we explore the potential of transfer learning from extensive language models and integrating multimodal information, such as emojis and images, to heighten the precision of sarcasm and irony detection. Rigorous evaluation against benchmark datasets and real-world social media content showcases the efficacy of our proposed models. The outcomes of this research hold paramount significance, offering a substantial advancement in comprehending intricate language nuances in digital communication. These findings carry profound implications for sentiment analysis, opinion mining, and an enhanced understanding of social media dynamics.

Daily Posture Behavior Patterns Derived From Multitime-Scale Topic Models Using Wearable Triaxial Acceleration for Assessment of Concern About Falling.

Concern about falling is prevalent in older population. This condition would cause a series of adverse physical and psychological consequences for older adults' health. Traditional assessment of concern about falling is relied on self-reported questionnaires and thus is too subjective. Therefore, we proposed a novel multi-time-scale topic modelling approach to quantitatively evaluate concern about falling by analyzing triaxial acceleration signals collected from a wearable pendent sensor. Different posture segments were firstly recognized to extract their corresponding feature subsets. Then, each selected feature related to concern about falling was clustered into discrete levels as feature letters of artificial words in different time scales. As a result, all older participants' signal recordings were converted to a collection of artificial documents, which can be processed by natural language processing methodologies. The topic modelling technique was used to discover daily posture behavior patterns from these documents as discriminants between older adults with different levels of concern about falling. The results indicated that there were significant differences in distributions of posture topics between groups of older adults with different levels of concern about falling. Additionally, the transitions of posture topics over daytime and nighttime revealed temporal regularities of posture behavior patterns of older adult's active and inactive status, which were substantially different for older adults with different levels of concern about falling. Finally, the level of concern about falling was accurately determined with accuracy of 71.2% based on the distributions of posture topics combined with the mobility performance metrics of walking behaviors and demographic information.

Information consistency as response to pre-launch advertising communications: The case of YouTube trailers

IntroductionPre-launch advertising communications are critical for the early adoption of experiential products. Often, companies release a variety of advertising messages for the same product, which results in a lack of information consistency. Research on the effect of advertising communications with different message content is scarce. Further, most studies on information consistency rely on experimental methods, leaving the actual effect of consumer response on product adoption unknown.MethodsTreating online comments to movie trailers as consumer response to advertising communication, we propose a natural language processing methodology to measure information consistency. We validate our measurement through an online experiment and test it on 1.3 million YouTube comments.ResultsOur empirical results provide evidence that information consistency driven by trailer-viewing is a key driver of opening box office success.DiscussionInsights deriving from this study are important to marketing communications research, especially in contexts where early product adoption is critical.

Text Mining Oral Histories in Historical Archaeology.

Advances in text mining and natural language processing methodologies have the potential to productively inform historical archaeology and oral history research. However, text mining methods are largely developed in the context of contemporary big data and publicly available texts, limiting the applicability of these tools in the context of historical and archaeological interpretation. Given the ability of text analysis to efficiently process and analyze large volumes of data, the potential for such tools to meaningfully inform historical archaeological research is significant, particularly for working with digitized data repositories or lengthy texts. Using oral histories recorded about a half-century ago from the anthracite coal mining region of Pennsylvania, USA, we discuss recent methodological developments in text analysis methodologies. We suggest future pathways to bridge the gap between generalized text mining methods and the particular needs of working with historical and place-based texts.

Connect with Student-Educational Consumer Brand Management Engagement: A Social Media Management Perspective

Social media empowers individuals to express their views regarding the events that take place in their day-to-day life. These events also incorporate the sentiments of students as well as educators towards their educational institutions. Thus, the study aims to analyze the sentiments of the students regarding their educational institutions to hike consumer brand management. However, Understanding the student at all levels of his being (physical, mental, emotional spiritual) is crucial to generate valuable learning and bringing forth a sense of belongingness. The study proposes to use social media platforms to connect, understand and engage the students at the professional as well as personal levels. Design/methodology/approach: Sentiment Analysis using vectorization and polarity has been applied to data collected from tweets of several leading universities of the world with their social media appearances to acquire significant insights into the objective of the study. During the analysis, it has been observed that symbol such as #hashtags and @ has played an intensified role in arousing the emotions of the individuals which eventually contributed to the branding of a particular designated university. Findings: Sentiment and emotional identification play a significant role in increasing brand value and nurturing the relationship between the student and the educator. It has been observed from the study that a sense of intensified community building is promoted which encourages new individuals to become a part of the reputed universities. Research Limitation/Implication: Since the field of education plays a great important role in the policies and economy of every country, students are the driving factors of this sector. Hence it is important to identify the emotion and sentiments of an individual regarding each small role that he goes through while studying. The university plays a very important role thus the study focuses on how each activity in the university affects the sentiments of a student and at the same time, it targets the nurturing of the student-educator relationship. The study focuses on how distinctive activities at the university level, their implication, and engagement at social media platforms affect the student's sentiments. The study is limited to the student-educator relationship. The present study contributes to the increase in consumer brand participation through emotion analysis and natural language processing methodology. Practical Implication: It refers to the fulfillment of certain conditions that if gets fulfilled will change reality to some extent. In this study, we have incorporated different tweets collected from several top universities across the world, since the data is in the form of tweets it has been processed to be subjected to analytical purposes. The practical implication for the study lies in the collection of datasets as the core of the study is the tweets that form the dataset. Social Implication: The social implication of the study is the representation of the sentiment of an individual regarding his or her university activities (Quality of Education, Research activities, etc.). After the analysis, there is a sense of pride has been observed among the student regarding the achievements of their educators and university. This not only inculcates a sense of belongingness among a group but also leads to the engagement of the student-educator relationship which further nurtures the policies of the educational universities to increase the brand value and consumer engagement. Originality/value: Engaging students on social media platforms, not only urge them to pursue it as a crucial medium of information but also bring a sense of pride to be part of their university. The study's findings not only contribute to the current understanding of sentiment analysis in brand engagement but are also important to develop a sense of belongingness in the students towards their educational institution which further nurtures the student-educator relationship management.

Coding energy knowledge in constructed responses with explainableNLPmodels

AbstractBackgroundFormative assessments are needed to enable monitoring how student knowledge develops throughout a unit. Constructed response items which require learners to formulate their own free‐text responses are well suited for testing their active knowledge. However, assessing such constructed responses in an automated fashion is a complex task and requires the application of natural language processing methodology. In this article, we implement and evaluate multiple machine learning models for coding energy knowledge in free‐text responses of German K‐12 students to items in formative science assessments which were conducted during synchronous online learning sessions.DatasetThe dataset we collected for this purpose consists of German constructed responses from 38 different items dealing with aspects of energy such as manifestation and transformation. The units and items were implemented with the help of project‐based pedagogy and evidence‐centered design, and the responses were coded for seven core ideas concerning the manifestation and transformation of energy. The data was collected from students in seventh, eighth and ninth grade.MethodologyWe train various transformer‐ and feature‐based models and compare their ability to recognize the respective ideas in students' writing. Moreover, as domain knowledge and its development can be formally modeled through knowledge networks, we evaluate how well the detection of the ideas within responses translated into accurate co‐occurrence‐based knowledge networks. Finally, in terms of the descriptive accuracy of our models, we inspect what features played a role for which prediction outcome and if the models pick up on undesired shortcuts. In addition to this, we analyze how much the models match human coders in what evidence within responses they consider important for their coding decisions.ResultsA model based on a modified GBERT‐large can achieve the overall most promising results, although descriptive accuracy varies much more than predictive accuracy for the different ideas assessed. For reasons of comparability, we also evaluate the same machine learning architecture using the SciEntsBank 3‐Way benchmark with an English RoBERTa‐large model, where it achieves state‐of‐the‐art results in two out of three evaluation categories.

MP46-05 CHARACTERIZING ONLINE TESTICULAR CANCER DISCUSSIONS WITH QUANTITATIVE THEMATIC ANALYSIS

You have accessJournal of UrologyCME1 May 2022MP46-05 CHARACTERIZING ONLINE TESTICULAR CANCER DISCUSSIONS WITH QUANTITATIVE THEMATIC ANALYSIS Krishna Ravivarapu, Alexander Small, Micah Levy, Chih Peng Chin, Evan Garden, Osama Al-Alao, Joseph Sewell Araya, and Michael Palese Krishna RavivarapuKrishna Ravivarapu More articles by this author , Alexander SmallAlexander Small More articles by this author , Micah LevyMicah Levy More articles by this author , Chih Peng ChinChih Peng Chin More articles by this author , Evan GardenEvan Garden More articles by this author , Osama Al-AlaoOsama Al-Alao More articles by this author , Joseph Sewell ArayaJoseph Sewell Araya More articles by this author , and Michael PaleseMichael Palese More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002612.05AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Patients are increasingly turning to the internet to crowdsource community support and medical advice. This is especially true for younger patient populations like those affected by testicular cancer. Extracting information from online discussions can facilitate better counseling and care by giving providers insights into prevailing concerns, common misconceptions, and shared emotions. We aim to use quantitative natural language processing methodology to identify key themes of discussion in one of the internet’s largest testicular cancer forums. METHODS: We extracted three years of posts (08/2016 to 08/2019) and comments (01/2017 to 12/2019) from the 3,593 member Reddit community r/testicularcancer. The dataset was analyzed by a meaning extraction method followed by principal component analysis (MEM/PCA) to computationally distill major themes of discussion (Figure 1). Kaiser-Meyer-Olkin Measure of Sampling Adequacy (>0.60) and Bartlett’s test of sphericity (<0.001) indicate that the data is suitable for MEM/PCA. RESULTS: 17,423 posts and comments were analyzed with MEM/PCA to generate five major themes of discussion, ranked by prevalence: 1) chemotherapy and side effects 2) medical evaluation after self-identifying an anomaly, 3) cancer diagnosis and surgery, 4) blood test result discussion of specific tumor markers, and 5) medical decision-making. Users seeking medical advice, such as questions about self-identified testicular anomalies (“lump” 5.2%), are consistently referred to medical evaluation (“urologist” 6.3%, “oncologist” 5.3%). Posts can be very technical, referring to forms of testicular cancer (“seminoma” 4.1%, “embryonal [carcinoma]” 1.1%, “teratoma” 1.1%, “yolk [sac] carcinoma” 0.6%, and “choriocarcinoma” 0.3%) as well as specific tumor markers (“AFP” 1.5%, “HCG” 1.1%, “LDH” 0.6%). Regarding “chemotherapy” (17.5%), “hair [loss]” (2.4%) is a major concern. CONCLUSIONS: Social media forums allow patients to express unbiased and uninhibited thoughts and concerns. These anecdotes can collectively influence patient attitudes and approaches. Therefore, it is important for providers to be aware of the key topics of discussion on social media to connect better with their patients, improving shared decision-making, patient satisfaction, and care. Source of Funding: None © 2022 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 207Issue Supplement 5May 2022Page: e774 Advertisement Copyright & Permissions© 2022 by American Urological Association Education and Research, Inc.MetricsAuthor Information Krishna Ravivarapu More articles by this author Alexander Small More articles by this author Micah Levy More articles by this author Chih Peng Chin More articles by this author Evan Garden More articles by this author Osama Al-Alao More articles by this author Joseph Sewell Araya More articles by this author Michael Palese More articles by this author Expand All Advertisement PDF DownloadLoading ...

Natural Language Processing Insight into LGBTQ+ Youth Mental Health During the COVID-19 Pandemic: Longitudinal Content Analysis of Anxiety-Provoking Topics and Trends in Emotion in LGBTeens Microcommunity Subreddit.

BackgroundWidespread fear surrounding COVID-19, coupled with physical and social distancing orders, has caused severe adverse mental health outcomes. Little is known, however, about how the COVID-19 crisis has impacted LGBTQ+ youth, who disproportionately experienced a high rate of adverse mental health outcomes before the COVID-19 pandemic.ObjectiveWe aimed to address this knowledge gap by harnessing natural language processing methodologies to investigate the evolution of conversation topics in the most popular subreddit for LGBTQ+ youth.MethodsWe generated a data set of all r/LGBTeens subreddit posts (n=39,389) between January 1, 2020 and February 1, 2021 and analyzed meaningful trends in anxiety, anger, and sadness in the posts. Because the distribution of anxiety before widespread social distancing orders was meaningfully different from the distribution after (P<.001), we employed latent Dirichlet allocation to examine topics that provoked this shift in anxiety.ResultsWe did not find any differences in LGBTQ+ youth anger and sadness before and after government-mandated social distancing; however, anxiety increased significantly (P<.001). Further analysis revealed a list of 10 anxiety-provoking topics discussed during the pandemic: attraction to a friend, coming out, coming out to family, discrimination, education, exploring sexuality, gender pronouns, love and relationship advice, starting a new relationship, and struggling with mental health.ConclusionsDuring the COVID-19 pandemic, LGBTQ+ teens increased their reliance on anonymous discussion forums when discussing anxiety-provoking topics. LGBTQ+ teens likely perceived anonymous forums as safe spaces for discussing lifestyle stressors during COVID-19 disruptions (eg, school closures). The list of prevalent anxiety-provoking topics in LGBTQ+ teens’ anonymous discussions can inform future mental health interventions in LGBTQ+ youth.

Social Media Mining for an Analysis of Nutrition and Dietary Health in Taiwan.

Dining is an essential part of human life. In order to pursue a healthier self, more and more people enjoy homemade cuisines. Consequently, the amount of recipe websites has increased significantly. These online recipes represent different cultures and cooking methods from various regions, and provide important indications on nutritional content. In recent years, the development of data science made data mining a popular research area. However, only a few researches in Taiwan have applied data mining in the studies of recipes and nutrients. Therefore, this work aims at utilizing machine learning models to discover health-related insights from recipes on social media. First, we collected over 15,000 Chinese recipes from the largest recipe website in Taiwan to build a recipe database. We then extracted information from this dataset through natural language processing methodologies so as to better understand the characteristics of various cuisines and ingredients. Thus, we can establish a classification model for the automatic categorization of recipes. We further performed cluster analysis for grouping nutrients to recognize the nutritional differences for each cluster and each cuisine type. The results showed that using the support vector machine (SVM) model can successfully classify recipes with an average F-score of 82%. We also analyzed the nutritional value of different cuisine categories and the possible health effects they may bring to the consumers. Our methods and findings can assist future work on extracting essential nutritional information from recipes and promoting healthier diets.

The electronic person‐specific outcome measure (ePSOM) development programme: Overall approach

AbstractBackgroundThe ePSOM development programme is a collaboration between the University of Edinburgh and Alzheimer’s Research UK prompted by the recognition that outcome measures currently used in clinical trials in prodromal and preclinical neurodegenerative diseases do not capture the research participants’ views of effectiveness. A better understanding of earlier manifestations of Alzheimer’s disease and the drive for relevant outcome measures, allied to technological advances in artificial intelligence, have mediated the electronic Person‐Specific Outcome Measure (ePSOM) development programme. The group took the view that ‘maintenance of brain health’ as opposed to ‘avoidance of symptoms’ would form the underpinning narrative in the ePSOM programme and ultimately app design.MethodThere are 4 sequential stages in the ePSOM programme (the first three completed): (1) literature review, (2) focus group study, (3) national survey, and (4) development of an app for capturing person‐specific outcomes. The survey data was analysed using clustering and natural language programming techniques and the results are presented separately. Here, we report the overall approach to the ePSOM programme.ResultOur literature review on patient‐reported outcome measures in the Alzheimer’s disease population showed trials currently do not use any patient‐reported outcome measures. Our focus groups with people living with memory problems; healthy volunteers and health care professionals (n=41) yielded five key domains in relation to what matters to people when developing new treatments for Alzheimer’s disease: Everyday Functioning; Relationships and Social Connections; Enjoying Life; Sense of Identity; and Alleviating Symptoms. Our online survey, which was created based on the literature review and focus groups, was completed by 5807 individuals. 30 clusters of themes emerged from the survey responses.ConclusionThe ePSOM development programme is building evidence in order to deliver the methodology for incorporating personally meaningful outcome measures in Alzheimer’s disease clinical trials. The completed three stages will underpin the ePSOM app, which will be using natural language processing methodologies, and have good psychometric properties enabling the app to be use in regulatory trials.

Improving productivity in Hollywood with data science: Using emotional arcs of movies to drive product and service innovation in entertainment industries

Improving productivity in the entertainment industry is a very challenging task as it heavily depends on generating attractive content for the consumers. The consumer-centric design (putting the consumers at the centre of the content development and production) focuses on ways in which businesses can design customized services and products which accurately reflect consumer preferences. We propose a new framework which allows to use data science to optimize content-generation in entertainment and test this framework for the motion picture industry. We use the natural language processing methodology combined with econometric analysis to explore whether and to what extent emotions shape consumer preferences for media and entertainment content, which, in turn, affect revenue streams. By analyzing 6,174 movie scripts, we generate the emotional trajectory of each motion picture. We then combine the obtained mappings into clusters which represent groupings of consumer emotional journeys. These clusters are then plugged into an econometric model to predict overall success parameters of the movies including box office revenues, viewer satisfaction levels (captured by IMDb ratings), awards, as well as the number of viewers’ and critics’ reviews. We find that emotional arcs in movies can be partitioned into 6 basic shapes. The highest box offices are associated with the Man in a Hole shape which is characterized by an emotional fall followed by an emotional rise. This U-shaped emotional arc results in financially successful movies irrespective of genre and production budget. Implications of this analysis for generating on-demand content and improving productivity in entertainment industries are discussed.

ADD: Academic Disciplines Detector Based on Wikipedia

The academic disciplines and their interrelationships represent a backbone that organizes the enormous amount of documented human knowledge available today. Having an up-to-date overview of the established disciplines, the emerging ones, and their mutual interactions is essential to the academic institutions, publishers, and many other actors involved in today's knowledge-based society, even in a situation of nonexistence of a precise definition of the term “academic discipline” itself. The discipline classification schemes represent crucial resources for the purpose, and in circumstances where the knowledge production rate demands discovering changes in their structure very frequently, the data-driven methodologies which facilitate their revision processes become essential. Analyzing the world-wide community's opinion on what represents a discipline, available through Wikipedia, can be very informative for the purpose, considering Wikipedia's comprehensiveness, continuous updates, and historical exports availability. This paper proposes a data-driven methodology for identification of the concepts which the world-wide community defines as disciplines at a particular moment by analyzing the information available in Wikipedia at that same moment. At the same time, it discusses Wikipedia's strengths and challenges on the task while also comparing a variety of Machine Learning and Natural Language Processing methodologies. High accuracy of the trained models is achieved on datasets created for this task specifically, and low changes in the model accuracy are observed on four Wikipedia exports from 2015 to 2018.

Territorial innovation models: to be or not to be, that’s the question

Industrial agglomerations are key in explaining the development paths followed by territories, particularly at sub-national levels. This field of research has received increasing attention in the last decades, what has led to the emergence of a variety of models intended to characterize innovation at the regional level. Moulaert and Sekia (Reg Stud 37:289–302, 2003) introduced the concept of ‘Territorial Innovation Models’ (TIMs) as a generic name that embraced these conceptual models of regional innovation in the literature. However, the literature does not help to assess the extent to which convergence or divergence is found across TIMs. In this paper we aim to clarify if there are clear boundaries across TIMs, so each TIM has particular characteristics that make it conceptually different from others, and hence, justify its introduction in the literature. Based on natural language processing methodologies, we extract the key terms of a large volume of academic papers published in peer review journals indexed in the Web of Science for the following TIMS: industrial districts, innovative milieu, learning regions, clusters, regional innovation systems, local production systems and new industrial spaces. We resort to Rapid Automatic Keyword Extraction to identify the associations between the topics extracted from the previous corpus. Finally, a configuration to visualise the results of the methodology followed is also proposed. Our results evidence that the previous models do not have a unique flavour but are rather similar in their taste. We evidence that there is quite little that is truly new in the different TIMs in terms of theory-building and the concepts being used in each model.

Predictions of novel Schistosoma mansoni - human protein interactions consistent with experimental data

Infection by the human blood fluke, Schistosoma mansoni involves a variety of cross-species protein- protein interactions. The pathogen expresses a diverse arsenal of proteins that facilitate the breach of physical and biochemical barriers present in skin evasion of the immune system, and digestion of human plasma proteins including albumin and hemoglobin, allowing schistosomes to reside in the host for years. However, only a small number of specific interactions between S. mansoni and human proteins have been identified. We present and apply a protocol that generates testable predictions of S. mansoni-human protein interactions. In this study, we have preliminary predictions of novel interactions between schistosome and human proteins relevant to infection and the ability of the parasite to evade the immune system. We applied a computational whole-genome comparative approach to predict potential S. mansoni-human protein interactions based on similarity to known protein complexes. We first predict S. mansoni -human protein interactions based on similarity to known protein complexes. Putative interactions were then scored and assessed using several contextual filters, including the use of annotation automatically derived from literature using a simple natural language processing methodology. Next, in vitro experiments were carried out between schistosome and host proteins to validate several prospective predictions. Our method predicted 7 out of the 10 previously known cross-species interactions involved in pathogenesis between S. mansoni and its human host. Interestingly, two novel putative interactions involving Schistosoma proteins, the cercarial elastase SmCE, and the adult tegument surface protein Sm29, were also predicted and experimentally characterized. Preliminary data suggest that elafin, a host endogenous serine protease inhibitor, may be a novel substrate for SmCE. Additionally, CD59, an inhibitor of the membrane attack complex, could interact with Sm29. Furthermore, the application framework provides an integrated methodology for investigation of host-pathogen interactions and an extensive source of orthogonal data for experimental analysis. We have made the predictions available for community perusal.

The Data Science of Hollywood: Using Emotional Arcs of Movies to Drive Business Model Innovation in Entertainment Industries

Much of business literature addresses the issues of consumer-centric design: how can businesses design customized services and products which accurately reflect consumer preferences? This paper uses data science natural language processing methodology to explore whether and to what extent emotions shape consumer preferences for media and entertainment content. Using a unique filtered dataset of 6,174 movie scripts, we generate a mapping of screen content to capture the emotional trajectory of each motion picture. We then combine the obtained mappings into clusters which represent groupings of consumer emotional journeys. These clusters are used to predict overall success parameters of the movies including box office revenues, viewer satisfaction levels (captured by IMDb ratings), awards, as well as the number of viewers’ and critics’ reviews. We find that like books all movie stories are dominated by 6 basic shapes. The highest box offices are associated with the Man in a Hole shape which is characterized by an emotional fall followed by an emotional rise. This shape results in financially successful movies irrespective of genre and production budget. Yet, Man in a Hole succeeds not because it produces most “liked” movies but because it generates most “talked about” movies. Interestingly, a carefully chosen combination of production budget and genre may produce a financially successful movie with any emotional shape. Implications of this analysis for generating on-demand content and for driving business model innovation in entertainment industries are discussed.