Online News Text Research Articles

Text Clustering Incremental Algorithm in Sensitive Topic Detection

With the rapid development of Internet technology, the influence of online consensus continues to expand. How to quickly and effectively discover sensitive topics and keep track of those topics has become an important research recently. Text clustering can aggregate news texts with the same or similar content to achieve the purpose of discovering topics automatically. Make improvement to clustering algorithm according to different media types is the main research direction. Although the existing typical clustering algorithms have certain advantages, they all face constraints on data size and data characteristics in specific applications. There is no existing algorithm can fully adapt to these characteristics. Although the application of more Single-pass algorithms in the (TDT) field can realize the discovery and tracking of topics, there are disadvantages of poor accuracy and slow speed under massive data. According to the dynamic evolution characteristics of online consensus, this paper proposes an incremental text clustering algorithm based on Single-pass, which optimizes the clustering accuracy and efficiency of massive news. Based on the real online news texts from the online consensus analysis system, we conduct an experiment to test and verify the feasibility and effectiveness of the algorithm we proposed. The result shows that the new algorithm is much more efficient compared to the original Single-pass clustering algorithm. In the real application, the new incremental text clustering algorithm basically meets the real-time demand of online topic detection and has a certain practical value.

Saving Tiger, Orangutan & Co: how subjective knowledge and text complexity influence online information seeking and behavior

ABSTRACTBesides others, subjective knowledge ‒ the feeling of being knowledgeable ‒ as well as the complexity of a communicated content have been shown to have an impact on different behavioral outcomes ‒ also in the field of consumer choices. However, it remains open how both factors influence subsequent outcomes such as information seeking, behavioral intentions, or actual choice behavior ‒ especially related to environmental issues. With a 2 (high/low subjective knowledge) × 2 (high/low complex information) between-subjects design (N = 87), we investigated the effects of subjective knowledge and complexity of an online news text about a specific environmental topic (i.e., palm oil) on behavioral intentions, online information seeking as well as on behavioral choices. Higher subjective knowledge raised the probability to volunteer for an NGO and the duration of time spent on a palm oil-related webpage. Environmental attitudes determined the general likelihood to engage in palm oil-related web search and the number of webpages visited. Text complexity did not influence the target variables.

Conflict of Deciding Zero Point Kilometer on Islamic Entrance in Indonesian Archipelago (Critical Discourse Analysis with Norman Fairclough Approach)

Critical discourse analysis (CDA) in this research came from two texts of online news. They were Medan Business Daily from Medan, North Sumatera and Kabar Pidie Jaya from Pidie, Aceh. Both of the news had the same theme, that was legitimation zero point kilometer of Islamic entrance in Indonesian archipelago. However, both of the news was conflicated, if it had not been finished as soon as possible, it would be a long conflict. Because of that, this CDA had a purpose to try for giving solution from another point of view. The point of view was from historical data about differences of point of view on the online news. The method of this study was by using Norman Fairclough approach with text analysis (description), discourse practice (interpretation), and social practice (explanation). The result of text analysis (description) was analyzed by using rhetoric pattern; modality, such as akan, bisa ; positive sentences; negative sentences; interrogative sentences; experiential and attitudinal vocabularies; life metaphor. Discourse practice analysis (interpretation) such as event series from the two online news texts. Explanative analysis followed the situational change, institution change, and social-cultural change which would be mediating this conflict based on the historical facts. Copyright (c) 2017 by KARSA. All right reserved DOI: 10.19105/karsa.v25i1.1223

Egyptian Arab Spring in CNN's Online News Reports… Supporting Peoples or Authorities is a Matter of Policy

This study aimed to explore Arab spring consequences that prevailed in Egypt after 2010-2011 as reflected by CNN in its online news article released in its website 2013. The main concern of the study is to unveil CNN's hidden ideologies towards Arab spring upheaval in Egypt investigating its discoursal ''Self'' and ''Other'' representations. To achieve this goal, the researcher utilized the following theories: 1) van Dijk's theory of Semantic Macrostructure (1980); to examine macro and micro structures of CNN's online news articles, 2) van Dijk's theory of Ideological Square (1998c); to examine CNN's ideologies embedded within its online news articles and 3) Wodak's Discourse- Historical Approach; to endorse linguistic and ideological analysis of CNN's online news article. Fairclough's three-dimensional Approach was utilized to organize the process of analysis. Within the linguistic analysis, macro and micro structures of CNN's online news articles were analyzed. At the macro level, the semantic macrostructure of CNN's text was outlined to determine its global meaning. At the micro level, the syntactic, lexical and rhetorical structures of CNN's text were examined to determine their local meaning. Within the ideological analysis, the CNN's online news text was analyzed to determine its ideological positive ''Self'' (in-group) and negative ''Other'' (out-group) presentations. The findings revealed that CNN's ideological orientations towards its positive "Self" and negative "Other" were varied as far as Egyptian conflict is concerned. Thus, it presented the Egyptian new authority as the positive identities while Muslim Brotherhood as the negative "Other'' identities.

Egyptian Arab Spring in CNN's Online News Reports… Supporting Peoples or Authorities is a Matter of Policy

This study aimed to explore Arab spring consequences that prevailed in Egypt after 2010-2011 as reflected by CNN in its online news article released in its website 2013. The main concern of the study is to unveil CNN's hidden ideologies towards Arab spring upheaval in Egypt investigating its discoursal ''Self'' and ''Other'' representations. To achieve this goal, the researcher utilized the following theories: 1) van Dijk's theory of Semantic Macrostructure (1980); to examine macro and micro structures of CNN's online news articles, 2) van Dijk's theory of Ideological Square (1998c); to examine CNN's ideologies embedded within its online news articles and 3) Wodak's Discourse- Historical Approach; to endorse linguistic and ideological analysis of CNN's online news article. Fairclough's three-dimensional Approach was utilized to organize the process of analysis. Within the linguistic analysis, macro and micro structures of CNN's online news articles were analyzed. At the macro level, the semantic macrostructure of CNN's text was outlined to determine its global meaning. At the micro level, the syntactic, lexical and rhetorical structures of CNN's text were examined to determine their local meaning. Within the ideological analysis, the CNN's online news text was analyzed to determine its ideological positive ''Self'' (in-group) and negative ''Other'' (out-group) presentations. The findings revealed that CNN's ideological orientations towards its positive Self and negative Other were varied as far as Egyptian conflict is concerned. Thus, it presented the Egyptian new authority as the positive identities while Muslim Brotherhood as the negative Other'' identities.

Sentiment analysis of online news text: a case study of appraisal theory

PurposeSentiment analysis and emotion processing are attracting increasing interest in many fields. Computer and information scientists are developing automated methods for sentiment analysis of online text. Most of the studies have focused on identifying sentiment polarity or orientation – whether a document, usually a product or movie review, carries a positive or negative sentiment. It is time for researchers to address more sophisticated kinds of sentiment analysis. This paper aims to evaluate a particular linguistic framework called appraisal theory for adoption in manual as well as automatic sentiment analysis of news text.Design/methodology/approachThe appraisal theory is applied to the analysis of a sample of political news articles reporting on Iraq and the economic policies of George W. Bush and Mahmoud Ahmadinejad to assess its utility and to identify challenges in adopting this framework.FindingsThe framework was useful in uncovering various aspects of sentiment that should be useful for researchers, such as the appraisers and object of appraisal, bias of the appraisers and the author, type of attitude and manner of expressing the sentiment. Problems encountered include difficulty in identifying appraisal phrases and attitude categories because of the subtlety of expression in political news articles, lack of treatment of tense and timeframe, lack of a typology of emotions, and need to identify different types of behaviours (political, verbal and material actions) that reflect sentiment.Originality/valueThe study has identified future directions for research in automated sentiment analysis as well as sentiment analysis of online news text. It has also demonstrated how sentiment analysis of news text can be carried out.

Developing a Disease Outbreak Event Corpus

BackgroundIn recent years, there has been a growth in work on the use of information extraction technologies for tracking disease outbreaks from online news texts, yet publicly available evaluation standards (and associated resources) for this new area of research have been noticeably lacking.ObjectiveThis study seeks to create a “gold standard” data set against which to test how accurately disease outbreak information extraction systems can identify the semantics of disease outbreak events. Additionally, we hope that the provision of an annotation scheme (and associated corpus) to the community will encourage open evaluation in this new and growing application area.MethodsWe developed an annotation scheme for identifying infectious disease outbreak events in news texts. An event─in the context of our annotation scheme─consists minimally of geographical (eg, country and province) and disease name information. However, the scheme also allows for the rich encoding of other domain salient concepts (eg, international travel, species, and food contamination).ResultsThe work resulted in a 200-document corpus of event-annotated disease outbreak reports that can be used to evaluate the accuracy of event detection algorithms (in this case, for the BioCaster biosurveillance online news information extraction system). In the 200 documents, 394 distinct events were identified (mean 1.97 events per document, range 0-25 events per document). We also provide a download script and graphical user interface (GUI)-based event browsing software to facilitate corpus exploration.ConclusionIn summary, we present an annotation scheme and corpus that can be used in the evaluation of disease outbreak event extraction algorithms. The annotation scheme and corpus were designed both with the particular evaluation requirements of the BioCaster system in mind as well as the wider need for further evaluation resources in this growing research area.