Automated Text Analysis and International Relations: The Introduction and Application of a Novel Technique for Twitter (original) (raw)
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Lecture Notes in Computer Science, 2013
With the emergence of smarthphones and social networks, a very large proportion of communication takes place on short texts. This type of communication, often anonymous, has allowed a new public participation in political issues. In particular, electoral phenomena all over the world have been greatly influenced by these networks. In the recent elections in Mexico, Twitter became a virtual place to bring together scientists, artists, politicians, adults, youth and students trying to persuade people about the candidate: Andrés Manuel López Obrador (AMLO). Our research is based on the collection of all tweets sent before, during and after the presidential elections of July 1, 2012 in Mexico containing the hashtag #AMLO. The aim of this study is to analyze the behavior of users on three different times. We apply SentiWordNet 3.0 in order to know how user behavior changes depending of the political situation and whether this is reflected on the tweets.
Computer-Assisted Content Analysis of Twitter Data
2014
Content analysis provides a useful and multifaceted, methodological framework for Twitter analysis. CAQDAS tools support the structuring of textual data by enabling categorising and coding. Depending on the research objective, it may be appropriate to choose a mixed-methods approach that combines... mehr Thesaurusschlagwörter Inhaltsanalyse; Twitter; Methode; Datengewinnung; computervermittelte Kommunikation Klassifikation Erhebungstechniken und Analysetechniken der Sozialwissenschaften Freie Schlagwörter CAQDAS Titel Sammelwerk, Herausgeberoder Konferenzband Twitter and Society Herausgeber Weller, Katrin; Bruns, Axel; Burgess, Jean; Mahrt, Merja; Puschmann, Cornelius Sprache Dokument Englisch Publikationsjahr 2014 Verlag P. Lang Erscheinungsort New York Seitenangabe S. 97-108 Schriftenreihe Digital Formations, 89 ISSN 1526-3169 ISBN 978-1-4539-1170-9 Computer-assisted content analysis of Twitter data SSOAR ▼ Browsen und suchen Dokument hinzufügen OAI-PMH-Schnittstelle
Procedia Computer Science, 2011
The relevance of web 2.0 and social networks has increased rapidly in the last years. As people use different networks for different purposes, communication is a common goal. Activities in virtual social networks result in a massive amount of information. User-generated content is clustered respect to platform, way of communication as well as quality and richness of content. Often the subject of information exchange deals with personal issues concerning events and individuals. Until now this information is not considered by means of classic information retrieval. Identifying distinct subjects, gathering and analyzing information and putting them into relationship at once are open scientific challenges. This contribution describes an approach to partial aspects of this problem by providing a prototype for full text analysis in social networks. Furthermore, based on a test scenario of analyzing postings in the field of political communication, potentials and limitations of the prototype are discussed.
2018 Elections Analysis in Turkey by using Twitter data
Contrary to traditional media, the influence of this new media regime is increasing day by day as the information flow is bi-directional and every citizen trying to reach the news is also a news source at the same time. Social media enable individuals to share knowledge, experiences, opinions, and ideas among each other. With regard to political sector, social media can be an enabler for participation and democracy among citizens. With the widespread use of social networks, participatory democracy practices are increasing, new forms of participation in the form of limited political participation by democracies the online political practices and the offline practices are intertwined and have important consequences in political life. Emotional analysis is an important field of study for generating meaningful information from large data sets. Emotion analysis objectively indicates whether a statement in a medium, which is used to extract meaningful information from this large data, is positive, negative or neutral. In other words, the content is the process of determining whether positives are negative or neutral. In this project, the emotion analysis method of the shares that the determined keywords pass through is analyzed.
Activeness of Syrian refugee crisis: an analysis of tweets
Social Network Analysis and Mining, 2019
In this paper, we propose and apply a method to analyze the activeness of an event based on related tweets. The method characterizes and measures activeness of an event by a set of indicators. The indicators proposed in this paper are original tweet count, retweet count, follower count, positive sentiment, negative sentiment, daily change in users count, and sparseness of user community. We present procedures to compute the last two indicators. All indicators collectively are used to determine the activeness of an event. This approach is used to analyze the Syrian-refugee-crisis-related tweets. Its generality is demonstrated by applying it to analyze "immigration"-related tweets.
2019
Sentiment Analysis (SA) or Opinion Mining (OM) is the computational study of people’s opinions, attitudes and emotions toward an entity. The entity can represent individuals, events or topics that are covered by reviews. There are issues with sentiment analysis for classification of text which has not yet been solved and it has been a challenge to many researchers. With the explosive growth of social media (e.g., reviews sites, forum discussions, blogs, micro-blogs, Twitter, comments, and postings in social network sites) on the Web, individuals and organizations are increasingly using the content in these media for decision making. The problem with sentiment Analysis is classifying the polarity of a given text at the document, sentence, or feature/aspect level, whether the expressed opinion in a document, sentence or an entity feature/aspect is positive, neutral or negative. Therefore, this study gave an overview of the different sentiment Analysis approaches. The study reviewed ex...
Tweet Analysis: Visualisation using Python
Research Square (Research Square), 2024
Most of the information is open to the public and can be mined. Huge amounts of data are available on the social networking site like Twitter. This data may be unstructured, semi-structured, or structured in any combination. This enables users to discuss concerns with various communities, as well as to share and express their opinions about various subjects. Sentiment analysis of Twitter data has been the subject of extensive research. Sentiment: that describes how people feel or think about a speci c subject. Not facts, but subjective perceptions of the domain are the result. Twitter is one of the most widely used platforms. Many scholars who conduct research in critical domains such as consumer brands, market performance, and democratic election prediction have found inspiration from Twitter. The initiative focuses on Twitter data analysis. This paper focuses on a tool to assess public sentiment, monitor crises, conduct market research, and the in uence and reach of a Twitter trend, it lets users view a range of metrics related to any hashtag, such as the most popular users utilizing it, users with the greatest reach, an estimate of the hashtag's actual and maximum reach, the timely frequency of tweets and retweets, and the estimated market value of the hashtag with the use of python and Twitter API. It will help combat misinformation and online negativity and promote accountability with a focus on visual content analysis.
Sentiment Analysis of the Syrian Conflict on Twitter
Medijske studije
Social media have become an important means of imposing ideas and interests in social conflicts. The Syrian conflict is analysed using sentiment analysis of tweets in order to establish how the sentiment shapes the modern political landscape and influences recipient knowledge. The importance of social networks and their potential in overthrowing regimes as well as in radicalization are highlighted. The authors suggest several stages that can be used for analysing tweets and how they impact the reader with selected narration. Sentiment analysis is used on a trained data set as a way to gain insight into tweets of different factions in the Syria conflict. Selected tweets on missile strikes were published on 14 April 2018 and the day after. The Twitter profiles of three different sides – pro-Assad, pro-West and anti- Assad – were also analysed. The results show that there is a real battle on social media with the purpose of influencing human emotions.
arXiv (Cornell University), 2021
The need for a comprehensive study to explore various aspects of online social media has been instigated by many researchers. This paper gives an insight into the social platform, Twitter. In this present work, we have illustrated stepwise procedure for crawling the data and discuss the key issues related to extracting associated features that can be useful in Twitter-related research while crawling these data from Application Programming Interfaces (APIs). Further, the data that comprises of over 86 million tweets have been analysed from various perspective including the most used languages, most frequent words, most frequent users, countries with most and least tweets and re-tweets, etc. The analysis reveals that the users' data associated with Twitter has a high affinity for researches in the various domain that includes politics, social science, economics, and linguistics, etc. In addition, the relation between Twitter users of a country and its human development index has been identified. It is observed that countries with very high human development indices have a relatively higher number of tweets compared to low human development indices countries. It is envisaged that the present study shall open many doors of researches in information processing and data science.
Anti-American Stance in Turkey: A Twitter Case Study
International Conference on Cyber Warfare and Security
The availability of social media and biased actors exacerbated Anti-American and Anti-Western views to extremes. In this paper, we report our efforts in analyzing anti-American views on Twitter. We have collected over three years of Turkish tweets related to the US, translated them into English, and analyzed these tweets using various computational social science tools. We found that Turkish tweets related to the US are significantly negative, and emotions reflect disgust and anger. Furthermore, we found that the source of the negative views stems from political actors like Trump or Biden rather than general hatred. Our results shed light on potential policy plans and interventions.