An artificial intelligence analysis of climate-change influencers' marketing on Twitter

Designing marketing strategies with social media influencers are becoming increasingly relevant for setting political agendas. This study focuses on how two representative social influencers, Greta Thunberg and Bill Gates, engage in advising against climate change. The investigation uses 23,294 tweets posted by them or their followers citing them on climate change around the 25th edition of the United Nations Climate Change Conference. This study applies artificial intelligence and natural language processing to analyse the marketing mechanism of social influencers. We scrutinize the sentiment of the messages and then identify and analyse the different networks constructed around them to discern how pervasive a social influencer's message is. The results show that Thunberg and Gates follow different and unconnected strategies to deliver their messages to their followers

Geomatics Applications to Contemporary Social and Environmental Problems in Mexico

Trends in geospatial technologies have led to the development of new powerful analysis and representation techniques that involve processing of massive datasets, some unstructured, some acquired from ubiquitous sources, and some others from remotely located sensors of different kinds, all of which complement the structured information produced on a regular basis by governmental and international agencies. In this chapter, we provide both an extensive revision of such techniques and an insight of the applications of some of these techniques in various study cases in Mexico for various scales of analysis: from regional migration flows of highly qualified people at the country level and the spatio-temporal analysis of unstructured information in geotagged tweets for sentiment assessment, to more local applications of participatory cartography for policy definitions jointly between local authorities and citizens, and an automated method for three dimensional (3D) modelling and visualisation of forest inventorying with laser scanner technology

Linking social media, medical literature, and clinical notes using deep learning.

Researchers analyze data, information, and knowledge through many sources, formats, and methods. The dominant data format includes text and images. In the healthcare industry, professionals generate a large quantity of unstructured data. The complexity of this data and the lack of computational power causes delays in analysis. However, with emerging deep learning algorithms and access to computational powers such as graphics processing unit (GPU) and tensor processing units (TPUs), processing text and images is becoming more accessible. Deep learning algorithms achieve remarkable results in natural language processing (NLP) and computer vision. In this study, we focus on NLP in the healthcare industry and collect data not only from electronic medical records (EMRs) but also medical literature and social media. We propose a framework for linking social media, medical literature, and EMRs clinical notes using deep learning algorithms. Connecting data sources requires defining a link between them, and our key is finding concepts in the medical text. The National Library of Medicine (NLM) introduces a Unified Medical Language System (UMLS) and we use this system as the foundation of our own system. We recognize social media’s dynamic nature and apply supervised and semi-supervised methodologies to generate concepts. Named entity recognition (NER) allows efficient extraction of information, or entities, from medical literature, and we extend the model to process the EMRs’ clinical notes via transfer learning. The results include an integrated, end-to-end, web-based system solution that unifies social media, literature, and clinical notes, and improves access to medical knowledge for the public and experts

Extracting Consumers’ Perceptions for Indonesian Spice Drinks Using Social Media Data Mining and Kansei Engineering

Local factors and global influences shape consumers’ perceptions through social media. In this regard, spice drinks as an agribusiness product have received increasing attention due to the Covid-19 pandemic. Therefore, understanding consumers’ perceptions is crucial for promoting the development of spice drinks. This study aims to (1) extract consumers’ perceptions of spice drinks based on discussions on social media using sentiment analysis and (2) classify the factors influencing their perceptions using factor analysis. The input dataset was obtained through Twitter data to acquire Kansei words. The results disclosed that Twitter could extract Kansei words and validate consumers’ perceptions of spice drinks as an agribusiness product. The sentiment analysis revealed 78% positive and 13% neutral tweets. Subsequently, an online survey was conducted among 495 respondents aged 18 to 41, distributed through various social media platforms from June to August 2022. The respondents were Generation Z and Millennials, with Generation Z referring to individuals born between 1997 and 2012 and Millennials born between 1981 and 1996. Factor analysis identified four principal components influencing consumers’ perceptions toward spice drinks: positive attitudes were associated with the quick, milky, mixed, healthy, quality, energy, fresh, warm, and safe; benefits were affiliated with the words enjoy, rest, life, smile, and story; quality concerned easy, flavour, and spicy; and sensory dealt with sweet, aroma, and bitter

Improving the user experience of open application programming interface (API) from a digital marketing perspective: A case study in a global telecommunications company

Application programming interface (API) is a programming interface that allows different applications to share information, functionality, and other resources with each other. When creating an open-source application programming interface, customer feedback is important. Understanding end-user needs can help improve the interface and target marketing activities. It is known from previous studies that quality of experience (QoE) is the driver for open radio access networks (O-RAN) and that user experience (UX) is what affects system use and leads to actual usage. This subject is relevant in this area of science since the software developer's perspective on open application programming interfaces is often disregarded, resulting in fewer studies. The objective of this thesis is to determine the necessary technological requirements to improve software developers' user experience and attract new customers. This research is based on an empirical study developing an open application programming interface for business-to-business (B2B) customers. The technology that is a central part of the study is programmable wireless network that allows to develop non-real time applications called xApps. This is case study research, which uses both quantitative and qualitative research methods. Information gathered for this research is going to be collected via interviews and surveys from the target group. This thesis's limitation is that the analyzed target group has a limited business market. The empirical data for this research is gathered from a global telecommunication company, from publications about the industry, and surveys and interviews gathered from the target market group. The three main findings of the thesis are related to how building trust among developers and partners is crucial, how developers need to be encouraged to learn something new and how developers that have more experience have fewer expectations. Previous research indicates that discovery about developers that have more experience have fewer expectations is new in this field of study. As a conclusion there are entry level blockages, telecommunication technology development, partner engagement, community, cost, and platform related killers that affect the motivation and prevent investing into an open API platform that specializes in xApps. To create a successful xApp platform the provider company needs to tackle these problems and highlight the possibilities that xApps offer.Sovellusohjelmointirajapinnan (API, Application programming interface) avulla eri sovellukset voivat jakaa tietoja, toimintoja ja muita resursseja keskenään. Avoimen lähdekoodin sovellusohjelmointirajapintaa luotaessa asiakaspalaute on tärkeää. Loppukäyttäjien tarpeiden ymmärtäminen voi auttaa parantamaan rajapintaa ja kohdentamaan markkinointia paremmin. Opinnäytetyön tavoitteena on selvittää, mitkä teknologiset vaatimukset ovat tarpeen ohjelmistokehittäjien käyttökokemuksen parantamiseksi ja uusien asiakkaiden houkuttelemiseksi. Aiemmista tutkimuksista tiedetään, että kokemuksen laatu (QoE) on avointen radioliityntäverkkojen (O-RAN) liikkeelle paneva voima ja, että käyttökokemus (UX) vaikuttaa järjestelmän käyttöön ja johtaa varsinaiseen käyttöön. Aiheella on merkitystä tällä tieteen alalla, koska ohjelmistokehittäjän näkökulma avoimiin sovellusohjelmointirajapintoihin jää usein huomiotta, jolloin ohjelmistokehittäjän näkökulmasta tietoa löytyy vähemmän. Tutkimus perustuu empiiriseen tutkimukseen, jossa kehitetään avoimen sovelluksen ohjelmarajapintaa yritysten välisille asiakkaille (B2B). Tutkimuksen keskeisenä teknologiana on ohjelmoitava langaton verkko, jonka avulla voidaan kehittää ei-reaaliaikaisia xApp-sovelluksia. Tämä tutkimus on tapaustutkimus, jossa on käytetty sekä kvantitatiivisia että kvalitatiivisia tutkimusmenetelmiä. Tutkimusta varten tietoa on kerätty kohderyhmän haastatteluilla ja kyselyillä. Tutkimuksen rajoituksena on, että analysoidulla kohderyhmällä on rajalliset liiketoimintamarkkinat. Empiirinen data tähän tutkimukseen on kerätty globaalista tietoliikenneyhtiöstä, alan julkaisuista sekä kohderyhmältä kerätyistä kyselyistä ja haastatteluista. Opinnäytetyön kolme päähavaintoa ovat, että luottamuksen rakentaminen kehittäjien ja kumppaneiden keskuudessa on ratkaisevan tärkeää, kehittäjiä tulee kannustaa oppimaan uutta ja kokeneemmilla kehittäjillä on vähemmän odotuksia. Aiemmat tutkimukset osoittavat, että löytö siitä, että kokeneemmilla kehittäjillä on vähemmän odotuksia, on uusi tällä tutkimusalalla. Johtopäätöksenä lähtötason pullonkaulat, viestintäteknologian kehitys, kumppanuussitoutuminen, yhteisön, kustannusten ja alustan tappajat vaikuttavat motivaatioon ja heikentävät investointia xAppeihin erikoistuneeseen avoimeen API-alustaan. Menestyvän xApp-alustan luomiseksi yrityksen on puututtava näihin ongelmiin ja tuotava esiin xAppien tarjoamat mahdollisuudet

Exploring Sentiment Analysis on Twitter: Investigating Public Opinion on Migration in Brazil from 2015 to 2020

openTechnology has reshaped societal interaction and the expression of opinions. Migration is a prominent trend, and analysing social media discussions provides insights into societal perspectives. This thesis explores how events between 2015 and 2020 impacted Brazilian sentiment on Twitter about migrants and refugees. Its aim was to uncover the influence of key sociopolitical events on public sentiment, clarifying how these echoed in the digital realm. Four key objectives guided this research: (a) understanding public opinions on migrants and refugees, (b) investigating how events influenced Twitter sentiment, (c) identifying terms used in migration-related tweets, and (d) tracking sentiment shifts, especially concerning changes in government. Sentiment analysis using VADER (Valence Aware Dictionary and sEntiment Reasoner) was employed to analyse tweet data. The use of computational methods in social sciences is gaining traction, yet no analysis has been conducted before to understand the sentiments of the Brazilian population regarding migration. The analysis underscored Twitter's role in reflecting and shaping public discourse, offering insights into how major events influenced discussions on migration. In conclusion, this study illuminated the landscape of Brazilian sentiment on migration, emphasizing the significance of innovative social media analysis methodologies for policymaking and societal inclusivity in the digital age

Interest-based segmentation of online video platforms' viewers using semantic technologies

To better connect supply and demand for various products, marketers needed novel ways to segment and target their customers with relevant adverts. Over the last decade, companies that collected a large amount of psychographic and behavioural data about their customers emerged as the pioneers of hyper-targeting. For example, Google can infer people’s interests based on their search queries, Facebook based on their thoughts, and Amazon by analysing their shopping cart history. In this context, the traditional channel used for advertising – the media market – saw its revenues plummeting as it failed to infer viewers’ interests based on the programmes they are watching, and target them with bespoke adverts. In order to propose a methodology for inferring viewers’ interests, this study adopted an interdisciplinary approach by analysing the problem from the viewpoint of three disciplines: Customer Segmentation, Media Market, and Large Knowledge Bases. Critically assessing and integrating the disciplinary insights was required for a deep understanding of: the reasons for which psychographic variables like interests and values are a better predictor for consumer behaviour as opposed to demographic variables; the various types of data collection and analysis methods used in the media industry; as well as the state of the art in terms of detecting concepts from text and linking them to various ontologies for inferring interests. Building on these insights, a methodology was proposed that can fully automate the process of inferring viewers interests by semantically analysing the description of the programmes they watch, and correlating it with data about their viewing history. While the methodology was deemed valid from a theoretical point of view, an extensive empirical validation was also undertaken for a better understanding of its applicability. Programme metadata for 320 programmes from a large broadcaster was analysed together with the viewing history of over 50,000 people during a three-year period. The findings from the validation were eventually used to further refine the methodology and show that is it possible not only to infer individual viewers interests based on the programmes watched, but also to cluster the audience based on their content consumption habits and track the performance of various topics in terms of attracting new viewers. Having an effective way to infer viewers’ interests has various applications for the media market, most notably in the areas of better segmenting and targeting audiences, developing content that matches viewers’ interests, or improving existing recommendation engine

Participative Urban Health and Healthy Aging in the Age of AI

This open access book constitutes the refereed proceedings of the 18th International Conference on String Processing and Information Retrieval, ICOST 2022, held in Paris, France, in June 2022. The 15 full papers and 10 short papers presented in this volume were carefully reviewed and selected from 33 submissions. They cover topics such as design, development, deployment, and evaluation of AI for health, smart urban environments, assistive technologies, chronic disease management, and coaching and health telematics systems

Deep Learning Detected Nutrient Deficiency in Chili Plant

Chili is a staple commodity that also affects the Indonesian economy due to high market demand. Proven in June 2019, chili is a contributor to Indonesia's inflation of 0.20% from 0.55%. One factor is crop failure due to malnutrition. In this study, the aim is to explore Deep Learning Technology in agriculture to help farmers be able to diagnose their plants, so that their plants are not malnourished. Using the RCNN algorithm as the architecture of this system. Use 270 datasets in 4 categories. The dataset used is primary data with chili samples in Boyolali Regency, Indonesia. The chili we use are curly chili. The results of this study are computers that can recognize nutrient deficiencies in chili plants based on image input received with the greatest testing accuracy of 82.61% and has the best mAP value of 15.57%