General Election and the Study of the Future

Indonesia's position in electoral development is getting better because of the legislation. It is different when a strong bargaining position is artificial. In this case, the state becomes strong because of its own efforts such as having sophisticated technology programs, producing sophisticated weapons, or having world-class athletes. The problem is when the candidate listed in the empty ballot has been elected by the community but the chosen one does not win, then such election is actually not of the will of the community. This study uses normative legal study design which means that it is normative juridical legal research. The approaches used in legal research are statute approach, case approach, and conceptual approach. Future elections will no longer change when there is no legal clarity in Indonesia if the robot is included in it. The election aimed at robots for is not being a contradiction but is a way out to produce elections that are truly fair. When we choose robots in the elections, artificial intelligence holds norms in society. Artificial intelligence will become a habit in Indonesia, turning to jus cogens because its main nature is indirect force

Field rules and bias in random surveys with quota samples. An assessment of CIS surveys

Surveys applying quota sampling in their final step are widely used in opinion and market research all over the world. This is also the case in Spain, where the surveys carried out by CIS (a public institution for sociological research supported by the government) have become a point of reference. The rules used by CIS to select individuals within quotas, however, could be improved as they lead to biases in age distributions. Analysing more than 545,000 responses collected in the 220 monthly barometers conducted between 1997 and 2016 by CIS, we compare the empirical distributions of the barometers with the expected distributions from the sample design and/or target populations. Among other results, we find, as a consequence of the rules used, significant overrepresentations in the observed proportions of respondents with ages equal to the minimum and maximum of each quota (age and gender group). Furthermore, in line with previous literature, we also note a significant overrepresentation of ages ending in zero. After offering simple solutions to avoid all these biases, we discuss some of their consequences for modelling and inference and about limitations and potentialities of CIS data.Peer Reviewe

PAAD: POLITICAL ARABIC ARTICLES DATASET FOR AUTOMATIC TEXT CATEGORIZATION

Now day’s text Classification and Sentiment analysis is considered as one of the popular Natural Language Processing (NLP) tasks. This kind of technique plays significant role in human activities and has impact on the daily behaviours. Each article in different fields such as politics and business represent different opinions according to the writer tendency. A huge amount of data will be acquired through that differentiation. The capability to manage the political orientation of an online article automatically. Therefore, there is no corpus for political categorization was directed towards this task in Arabic, due to the lack of rich representative resources for training an Arabic text classifier. However, we introduce political Arabic articles dataset (PAAD) of textual data collected from newspapers, social network, general forum and ideology website. The dataset is 206 articles distributed into three categories as (Reform, Conservative and Revolutionary) that we offer to the research community on Arabic computational linguistics. We anticipate that this dataset would make a great aid for a variety of NLP tasks on Modern Standard Arabic, political text classification purposes. We present the data in raw form and excel file. Excel file will be in four types such as V1 raw data, V2 preprocessing, V3 root stemming and V4 light stemming

A retrospective on state of the art social media research methods: Ethical decisions, big-small data rivalries and the spectre of the 6Vs

This concluding chapter offers critical reflections on some of the key themes covered in the Handbook. Ethics emerged as a concern for many scholars, both for those engaging in quantitative and qualitative approaches. Scholars agree in that there is no overarching set of rules that can be applied to all projects blindly, rather they see ethical decisions as being grounded in the specifics of the data being collected, the social group under study, and the potential repercussions for subjects. A second central theme was the value of qualitative approaches for understanding ‘anomalies’ within larger data sets. Qualitative approaches are seen as valuable and a stand-alone means of collecting, analyzing and making sense of social media data, in particular for projects where context is essential. Finally, as the contributions in this volume demonstrate that many of the challenges posed by the nature of social media data are being tackled and addressed, this chapter ends with a reorientation of the 6Vs which focuses on the primacy of the researcher in the decision-making process. We argue that the provision of technical solutions alone do not entirely address the 6V problem and clarity of thought around research design is still just as important as ever

Text annotation using textual semantic similarity and term-frequency (Twitter)

Researchers on social-media understandably assert that the contributions social media has made on various sectors is massive. Business development managers today have directed a huge amount of effort in strategizing efficient collaboration with both customers and other organizations using social-media. Despite the visible impact social media has made, a lot of digitally shared information is yet to be revealed. Gradually twitter has become the main hub for many Information system researchers, because tweets can freely be accessible in real-time by any one. Motivated by earlier studies where IS researchers addressed big-data analysis and management by employing content analysis techniques, this paper proposes a novel approach to perform unsupervised classification of the tweets into different labels. It introduces a unique algorithm that uses semantic similarity between texts, Term-frequency and a determinant threshold to perform content analysis. The goal of this approach is to extract relevant features from a tweet thus reducing dimension and preparing training datasets that would be used to build classifiers

Using Social Media to Enhance Survey Data

This article provides an overview on the roles of social media (SM) in survey research. After examining the characteristics and challenges of using social media data in statistical research, we discuss recent approaches on ways SM have been used to enhance survey research. We then introduce a general modular framework for producing statistics taking advantage of the two data sources. Finally, we highlight important questions for future research

Developing a Prototype System for Syndromic Surveillance and Visualization Using Social Media Data.

Syndromic surveillance of emerging diseases is crucial for timely planning and execution of epidemic response from both local and global authorities. Traditional sources of information employed by surveillance systems are not only slow but also impractical for developing countries. Internet and social media provide a free source of a large amount of data which can be utilized for Syndromic surveillance. We propose developing a prototype system for gathering, storing, filtering and presenting data collected from Twitter (a popular social media platform). Since social media data is inherently noisy we describe ways to preprocess the gathered data and utilize SVM (Support Vector Machine) to identify tweets relating to influenza like symptoms. The filtered data is presented in a web application, which allows the user to explore the underlying data in both spatial and temporal dimensions