Verifying baselines for crisis event information classification on Twitter

Social media are rich information sources during and in the aftermath of crisis events such as earthquakes and terrorist attacks. Despite myriad challenges, with the right tools, significant insight can be gained which can assist emergency responders and related applications. However, most extant approaches are incomparable, using bespoke definitions, models, datasets and even evaluation metrics. Furthermore, it is rare that code, trained models, or exhaustive parametrisation details are made openly available. Thus, even confirmation of self-reported performance is problematic; authoritatively determining the state of the art (SOTA) is essentially impossible. Consequently, to begin addressing such endemic ambiguity, this paper seeks to make 3 contributions: 1) the replication and results confirmation of a leading (and generalisable) technique; 2) testing straightforward modifications of the technique likely to improve performance; and 3) the extension of the technique to a novel and complimentary type of crisis-relevant information to demonstrate it’s generalisability

From past to present: spam detection and identifying opinion leaders in social networks

On microblogging sites, which are gaining more and more users every day, a wide range of ideas are quickly emerging, spreading, and creating interactive environments. In some cases, in Turkey as well as in the rest of the world, it was noticed that events were published on microblogging sites before appearing in visual, audio and printed news sources. Thanks to the rapid flow of information in social networks, it can reach millions of people in seconds. In this context, social media can be seen as one of the most important sources of information affecting public opinion. Since the information in social networks became accessible, research started to be conducted using the information on the social networks. While the studies about spam detection and identification of opinion leaders gained popularity, surveys about these topics began to be published. This study also shows the importance of spam detection and identification of opinion leaders in social networks. It is seen that the data collected from social platforms, especially in recent years, has sourced many state-of-art applications. There are independent surveys that focus on filtering the spam content and detecting influencers on social networks. This survey analyzes both spam detection studies and opinion leader identification and categorizes these studies by their methodologies. As far as we know there is no survey that contains approaches for both spam detection and opinion leader identification in social networks. This survey contains an overview of the past and recent advances in both spam detection and opinion leader identification studies in social networks. Furthermore, readers of this survey have the opportunity of understanding general aspects of different studies about spam detection and opinion leader identification while observing key points and comparisons of these studies.This work is supported in part by the Scientific and Technological Research Council of Turkey (TUBITAK) through grant number 118E315 and grant number 120E187. Points of view in this document are those of the authors and do not necessarily represent the official position or policies of TUBITAK.Publisher's VersionEmerging Sources Citation Index (ESCI)Q4WOS:00080858480001

Multilingual sentiment analysis in social media.

252 p.This thesis addresses the task of analysing sentiment in messages coming from social media. The ultimate goal was to develop a Sentiment Analysis system for Basque. However, because of the socio-linguistic reality of the Basque language a tool providing only analysis for Basque would not be enough for a real world application. Thus, we set out to develop a multilingual system, including Basque, English, French and Spanish.The thesis addresses the following challenges to build such a system:- Analysing methods for creating Sentiment lexicons, suitable for less resourced languages.- Analysis of social media (specifically Twitter): Tweets pose several challenges in order to understand and extract opinions from such messages. Language identification and microtext normalization are addressed.- Research the state of the art in polarity classification, and develop a supervised classifier that is tested against well known social media benchmarks.- Develop a social media monitor capable of analysing sentiment with respect to specific events, products or organizations

Appraisal System on Twitter: An Attitudinal Analysis Toward Alleged Islamic Blasphemy Case of M Kece

This study examines the public's assessment of an Islamic blasphemy case named M. Kece on Twitter. It is a qualitative descriptive study. The data is a public assessment regarding the Islamic blasphemy case named M. Kece on Twitter using Appraisal Theory (Martin & White, 2005). Focusing on Attitudinal Analysis, the result shows the Attitudinal Analysis in social media, precisely Twitter, on the alleged Islamic blasphemy of M. Kece. Judgment is the most dominant, with as much as 54% over Affect (19%) and Appreciation (27%). In all three, more than three fourth of the data are negative. This dominant negative appraisal of netizens of Twitter users shows the public's assessment of what M. Kece has done, namely the content of his YouTube channel, which was reported and imprisoned due to a blasphemy case. This study affirmed that all content creators must be careful about the content they post. On the other hand, the viewer also must be selective about the content they access on social media not to be misleading. AbstrakStudi ini mengkaji penilaian publik terhadap kasus penistaan agama Islam bernama M. Kece di Twitter. Penelitian ini menggunakan pendekatan deskriptif kualitatif. Data dalam penelitian ini adalah penilaian publik terkait kasus penistaan agama Islam bernama M. Kece di Twitter dengan menggunakan Appraisal Theory (Martin & White, 2005). Berfokus pada Analisis Sikap, hasilnya menunjukkan Analisis Sikap di media sosial, tepatnya Twitter, tentang dugaan penistaan agama Islam terhadap M. Kece. Afeksi adalah yang paling dominan, dengan sebanyak 54% di atas judgment (19%) dan Apresiasi (27%). Dari ketiga kategori ini, lebih dari tiga perempat data merupakan penialaian negatif. Dominasi penilaian netizen pengguna Twitter ini menunjukkan penilaian mereka terhadap apa yang telah dilakukan M. Kece, yaitu konten channel YouTube-nya yang dilaporkan dan dipenjara akibat kasus penistaan agama. Penelitian ini menegaskan bahwa semua pembuat konten harus berhati-hati dengan konten yang mereka posting. Di sisi lain, penonton juga harus selektif terhadap konten yang mereka akses di media sosial agar tidak menyesatkan