Detecting and Monitoring Hate Speech in Twitter

Social Media are sensors in the real world that can be used to measure the pulse of societies. However, the massive and unfiltered feed of messages posted in social media is a phenomenon that nowadays raises social alarms, especially when these messages contain hate speech targeted to a specific individual or group. In this context, governments and non-governmental organizations (NGOs) are concerned about the possible negative impact that these messages can have on individuals or on the society. In this paper, we present HaterNet, an intelligent system currently being used by the Spanish National Office Against Hate Crimes of the Spanish State Secretariat for Security that identifies and monitors the evolution of hate speech in Twitter. The contributions of this research are many-fold: (1) It introduces the first intelligent system that monitors and visualizes, using social network analysis techniques, hate speech in Social Media. (2) It introduces a novel public dataset on hate speech in Spanish consisting of 6000 expert-labeled tweets. (3) It compares several classification approaches based on different document representation strategies and text classification models. (4) The best approach consists of a combination of a LTSM+MLP neural network that takes as input the tweet’s word, emoji, and expression tokens’ embeddings enriched by the tf-idf, and obtains an area under the curve (AUC) of 0.828 on our dataset, outperforming previous methods presented in the literatureThe work by Quijano-Sanchez was supported by the Spanish Ministry of Science and Innovation grant FJCI-2016-28855. The research of Liberatore was supported by the Government of Spain, grant MTM2015-65803-R, and by the European Union’s Horizon 2020 Research and Innovation Programme, under the Marie Sklodowska-Curie grant agreement No. 691161 (GEOSAFE). All the financial support is gratefully acknowledge

Is there a Correlation Between Wikidata Revisions and Trending Hashtags on Twitter?

Twitter is a microblogging application used by its members to interact and stay socially connected by sharing instant messages called tweets that are up to 280 characters long. Within these tweets, users can add hashtags to relate the message to a topic that is shared among users. Wikidata is a central knowledge base of information relying on its members and machines bots to keeping its content up to date. The data is stored in a highly structured format with the added SPARQL protocol and RDF Query Language (SPARQL) endpoint to allow users to query its knowledge base

Using Twitter to Understand Public Interest in Climate Change: The case of Qatar

Climate change has received an extensive attention from public opinion in the last couple of years, after being considered for decades as an exclusive scientific debate. Governments and world-wide organizations such as the United Nations are working more than ever on raising and maintaining public awareness toward this global issue. In the present study, we examine and analyze Climate Change conversations in Qatar's Twittersphere, and sense public awareness towards this global and shared problem in general, and its various related topics in particular. Such topics include but are not limited to politics, economy, disasters, energy and sandstorms. To address this concern, we collect and analyze a large dataset of 109 million tweets posted by 98K distinct users living in Qatar -- one of the largest emitters of CO2 worldwide. We use a taxonomy of climate change topics created as part of the United Nations Pulse project to capture the climate change discourse in more than 36K tweets. We also examine which topics people refer to when they discuss climate change, and perform different analysis to understand the temporal dynamics of public interest toward these topics.Comment: Will appear in the proceedings of the International Workshop on Social Media for Environment and Ecological Monitoring (SWEEM'16

Semantic network analysis of Twitter data and their psycho-social implications

To study the language of social networks and its psycho-social implications there is a need for an interdisciplinary approach that combines scientists from the fields of network science, psychology and linguistics. This thesis is a product of the author's work undertaken in two such interdisciplinary projects, Comparing the role of men within prolife and prochoice community in discussion about abortion on Twitter and Analyzing political discourse before and after election using Tweets posted by the candidates for the 2020 US Elections. Both projects used tweets in English language from various users collected over a predefined time span. Twitter data offers various possibilities for interpretation within the context of network science. The focus of this thesis was to study a special kind of networks, the semantic networks, built from the tweet text, hashtags and metadata. More specifically, we detected meaningful communities based on the topics in order to study the language used within the context of those topics. Although in both projects tweets are used to study language on social media and its psycho-social implications, the main goal differs and so does the methodology applied to community detection

Analyzing tourist data on Twitter: a case study in the province of Granada at Spain

This work has been funded by the Spanish Ministerio de Economía y Competitividad under project TIN2016-77902-C3-2-P, and the European Regional Development Fund (ERDF-FEDER)