Feat: A Facebook Extraction And Analysis Toolkit

Social media usage has become mainstream. According to a recent study done by Edison Research in 2016, 78% of the U.S. population has a social media profile [8]. The number of active Facebook users is over one billion. In addition, 71% of adults use Facebook, which is the target of this thesis. Because Facebook is so widely used, it is also a popular medium for those wanting to promote their products and ideas, including presidential candidates. Many researchers have extracted data from social media sites, including Facebook, to predict the outcome of elections, to predict election turnout by political party, and to determine voter opinions. This thesis will discuss the development and use of a suite of tools for gathering and analyzing data collected from the social media site, Facebook. Although the suite of tools can be used to collect data from any public Facebook site, this thesis will specifically focus on using the tools to extract data from the pages of presidential candidates. In addition to extracting Facebook data and storing the data in a database, tools in the suite can be used to analyze and visualize the collected data

Prediction of the 2023 Turkish Presidential Election Results Using Social Media Data

Social media platforms influence the way political campaigns are run and therefore they have become an increasingly important tool for politicians to directly interact with citizens. Previous elections in various countries have shown that social media data may significantly impact election results. In this study, we aim to predict the vote shares of parties participating in the 2023 elections in Turkey by combining social media data from various platforms together with traditional polling data. Our approach is a volume-based approach that considers the number of social media interactions rather than content. We compare several prediction models across varying time windows. Our results show that for all time windows, the ARIMAX model outperforms the other algorithms.Comment: 25 pages, 7 tables, 3 figure

SENTIMENTALNA ANALIZA SADRŽAJA DRUŠTVENIH MEDIJA HRVATSKE HOTELSKE INDUSTRIJE

While social media have become a daily routine in modern society, brand communication and engagement with customers have become essential elements of marketing strategy and success in the tourism and hotel industry. This revolution of social media, in tourism and hospitality marketing, contributed to the rise of a novel sentiment analysis from a machine learning and natural language processing point of view. The purpose of the study is: to provide a general descriptive overview of comments posted by Facebook page followers; to identify specific textual attributes of hotel brand posts on social media and to apply the sentiment analysis to Facebook comments from four- and five-star hotel brands in Croatia to identify and compare customers\u27 feelings and attitudes towards the staff, services and products that hotel brands promote by posting messages on Facebook pages. To analyse hotel brand sentiments, the authors collected a total of 4,248 comments and 2,373 postings in English, German and Italian. The results showed that the comments on four- and five-star hotel brands expressed predominantly positive sentiments. Despite the positively oriented sentiments in the comments, Facebook page followers are predominantly passive users and do not tend to comment actively. The results can be used by marketers in the tourism and hospitality industry to plan their future social media communication strategies.Iako su društveni mediji postali svakodnevica u modernom društvu, brend komuniciranje i uključenost potrošača postali su ključni elementi marketinške strategije i uspjeha u sektoru turizma i ugostiteljstva. Revolucija društvenih medija, u marketingu, turizmu i ugostiteljstvu, pridonijela je razvoju sentimentalne analize sa stajališta strojnog učenja i obrade prirodnog jezika. Svrha ovog rada je: pružiti opći deskriptivni pregled komentara objavljenih od strane pratitelja Facebook stranice; identificirati specifične tekstualne karatkeristike objava hotelskih brendova na Facebook društvenoj mreži i primijeniti sentimentalnu analizu nad Facebook komentarima hotelskih brendova s četiri i pet zvjezdica u Hrvatskoj kako bi se identificirali i usporedili osjećaji, mišljenja i stavovi kupaca prema osoblju, uslugama i proizvodima koje hotelski brendovi promoviraju objavljivanjem poruka na Facebook stranicama. Da bi se analizirali sentimenti komentara pratitelja hotelskih brendova na Facebook društvenoj mreži, autori su prikupili ukupno 4.248 komentara i 2.373 objave na engleskom, njemačkom i talijanskom jeziku. Rezultati su pokazali da su komentari na stranicama hotelskih brendova imali pretežno pozitivan sentiment. Unatoč pozitivno orijentiranim osjećajima u komentarima, pratitelji Facebook stranica su uglavnom pasivni korisnici i ne sudjeluju aktivno u komentiraju objava. Rezultati mogu koristiti marketinškim stručnjacima u turizmu i ugostiteljstvu za planiranje budućih strategija komunikacije putem društvenih media

Politische Maschinen: Maschinelles Lernen für das Verständnis von sozialen Maschinen

This thesis investigates human-algorithm interactions in sociotechnological ecosystems. Specifically, it applies machine learning and statistical methods to uncover political dimensions of algorithmic influence in social media platforms and automated decision making systems. Based on the results, the study discusses the legal, political and ethical consequences of algorithmic implementations.Diese Arbeit untersucht Mensch-Algorithmen-Interaktionen in sozio-technologischen Ökosystemen. Sie wendet maschinelles Lernen und statistische Methoden an, um politische Dimensionen des algorithmischen Einflusses auf Socialen Medien und automatisierten Entscheidungssystemen aufzudecken. Aufgrund der Ergebnisse diskutiert die Studie die rechtlichen, politischen und ethischen Konsequenzen von algorithmischen Anwendungen

Sentiment Analysis for Social Media

Sentiment analysis is a branch of natural language processing concerned with the study of the intensity of the emotions expressed in a piece of text. The automated analysis of the multitude of messages delivered through social media is one of the hottest research fields, both in academy and in industry, due to its extremely high potential applicability in many different domains. This Special Issue describes both technological contributions to the field, mostly based on deep learning techniques, and specific applications in areas like health insurance, gender classification, recommender systems, and cyber aggression detection

Counterpublic Discourses in Facebook Comment Sections — A Comparative Analysis of Norwegian, Swedish, and Danish (Social) Media Discourse on Islam

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Catalyst of hate? Ethnic insulting on YouTube in the aftermath of terror attacks in France, Germany and the United Kingdom 2014–2017

In the last 20 years, several major terror attacks conducted in the name of political Islam hit Western Europe. We examine the impact of such terror attacks on hostile behaviour on social media from a cross-national perspective. To this end, we draw upon time-stamped, behavioural data from YouTube and focus on the frequency and popularity (‘likes’) of ethnically insulting comments among a corpus of approximately one hundred thousand comments. We study aggregate change and use individual-level panel data to investigate within-user change in ethnic insulting in periods leading up to and following major terror events in Germany, France and the UK. Results indicate that terror attacks boost interest in immigration-related topics in general, and lead to a disproportional increase in hate speech in particular. Moreover, we find that attack effects spill over to other countries in several, but not all, instances. Deeper analyses suggest, however, that this pattern is mainly driven by changes in the composition of users and not by changing behaviour of individual users. That is, a surge in ethnic insulting comes from hateful users newly entering online discussions, rather than previous users becoming more hateful following an attack

Distant Supervised Construction and Evaluation of a Novel Dataset of Emotion-Tagged Social Media Comments in Spanish

Tagged language resources are an essential requirement for developing machine-learning text-based classifiers. However, manual tagging is extremely time consuming and the resulting datasets are rather small, containing only a few thousand samples. Basic emotion datasets are particularly difficult to classify manually because categorization is prone to subjectivity, and thus, redundant classification is required to validate the assigned tag. Even though, in recent years, the amount of emotion-tagged text datasets in Spanish has been growing, it cannot be compared with the number, size, and quality of the datasets in English. Quality is a particularly concerning issue, as not many datasets in Spanish included a validation step in the construction process. In this article, a dataset of social media comments in Spanish is compiled, selected, filtered, and presented. A sample of the dataset is reclassified by a group of psychologists and validated using the Fleiss Kappa interrater agreement measure. Error analysis is performed by using the Sentic Computing tool BabelSenticNet. Results indicate that the agreement between the human raters and the automatically acquired tag is moderate, similar to other manually tagged datasets, with the advantages that the presented dataset contains several hundreds of thousands of tagged comments and it does not require extensive manual tagging. The agreement measured between human raters is very similar to the one between human raters and the original tag. Every measure presented is in the moderate agreement zone and, as such, suitable for training classification algorithms in sentiment analysis field.Fil: Tessore, Juan Pablo. Universidad Nacional del Noroeste de la Pcia.de Bs.as.. Escuela de Tecnologia. Instituto de Investigacion y Transferencia En Tecnologia. - Comision de Investigaciones Cientificas de la Provincia de Buenos Aires. Instituto de Investigacion y Transferencia En Tecnologia.; ArgentinaFil: Esnaola, Leonardo Martín. Universidad Nacional del Noroeste de la Pcia.de Bs.as.. Escuela de Tecnologia. Instituto de Investigacion y Transferencia En Tecnologia. - Comision de Investigaciones Cientificas de la Provincia de Buenos Aires. Instituto de Investigacion y Transferencia En Tecnologia.; ArgentinaFil: Lanzarini, Laura Cristina. Universidad Nacional de La Plata. Facultad de Informática. Instituto de Investigación en Informática Lidi; ArgentinaFil: Baldassarri, Sandra Silvia. Universidad de Zaragoza; Españ

Distant Supervised Construction and Evaluation of a Novel Dataset of Emotion-Tagged Social Media Comments in Spanish

Tagged language resources are an essential requirement for developing machine-learning text-based classifiers. However, manual tagging is extremely time consuming and the resulting datasets are rather small, containing only a few thousand samples. Basic emotion datasets are particularly difficult to classify manually because categorization is prone to subjectivity, and thus, redundant classification is required to validate the assigned tag. Even though, in recent years, the amount of emotion-tagged text datasets in Spanish has been growing, it cannot be compared with the number, size, and quality of the datasets in English. Quality is a particularly concerning issue, as not many datasets in Spanish included a validation step in the construction process. In this article, a dataset of social media comments in Spanish is compiled, selected, filtered, and presented. A sample of the dataset is reclassified by a group of psychologists and validated using the Fleiss Kappa interrater agreement measure. Error analysis is performed by using the Sentic Computing tool BabelSenticNet. Results indicate that the agreement between the human raters and the automatically acquired tag is moderate, similar to other manually tagged datasets, with the advantages that the presented dataset contains several hundreds of thousands of tagged comments and it does not require extensive manual tagging. The agreement measured between human raters is very similar to the one between human raters and the original tag. Every measure presented is in the moderate agreement zone and, as such, suitable for training classification algorithms in sentiment analysis field