Computational Politics is the study of computational methods to analyze and moderate users' behaviors related to political activities such as election campaign persuasion, political affiliation, and opinion mining. With the rapid development and ease of access to the Internet, Information Communication Technologies (ICT) have given rise to massive numbers of users joining online communities and the digitization of political practices such as debates. These communities and digitized data contain both explicit and latent information about users and their behaviors related to politics and social movements. For researchers, it is essential to utilize data from these sources to develop and design systems that not only provide solutions to computational politics but also help other businesses, such as marketers, to increase users' participation and interactions. In this survey, we attempt to categorize main areas in computational politics and summarize the prominent studies in one place to better understand computational politics across different and multidimensional platforms. e.g., online social networks, online forums, and political debates. We then conclude this study by highlighting future research directions, opportunities, and challenges.

National bibliographies have been identified as a crucial resource for historical research on the publishing landscape, but using them requires addressing challenges of data quality, completeness, and interpretation. We call this approach bibliographic data science. In this article, we briefly assess the development of book formats and the vernacularization process in early modern Europe. The work undertaken paves the way for more extensive integration of library catalogs to map the history of the book.

Historical scholarship is currently undergoing a digital turn. All historians have experienced this change in one way or another, by writing on word processors, applying quantitative methods on digitalized source materials, or using internet resources and digital tools. Digital Histories showcases this emerging wave of digital history research. It presents work by historians who – on their own or through collaborations with e.g. information technology specialists – have uncovered new, empirical historical knowledge through digital and computational methods. The topics of the volume range from the medieval period to the present day, including various parts of Europe. The chapters apply an exemplary array of methods, such as digital metadata analysis, machine learning, network analysis, topic modelling, named entity recognition, collocation analysis, critical search, and text and data mining. The volume argues that digital history is entering a mature phase, digital history ‘in action’, where its focus is shifting from the building of resources towards the making of new historical knowledge. This also involves novel challenges that digital methods pose to historical research, including awareness of the pitfalls and limitations of the digital tools and the necessity of new forms of digital source criticisms. Through its combination of empirical, conceptual and contextual studies, Digital Histories is a timely and pioneering contribution taking stock of how digital research currently advances historical scholarship.

The corpus of historical newspapers and journals published in Finland, with more than 11 million pages of historical text, is of great value to the research community. The National Library of Finland (NLF) has OCRed the corpus with ABBYY FineReader, a commercial software that provides OCR models pre-trained on general historical fonts. The estimated accuracy of the OCRed text is between 87% - 92% on the character level, which is rather low even for scientific research. Optical character recognition of printed text commonly reaches over 99% accuracy for modern Latin fonts. Historical documents, on the other hand, contain a large variety of fonts, can be of poor condition and often are written without an orthographic standard (the same words are spelled differently). All these reasons present a challenge to creating robust and highly accurate OCR models for historical data. The corpus of historical newspapers and journals published in Finland is particularly challenging because it is written in both the official languages of Finland (Finnish and Swedish) and is printed in two font-families (Blackletter and Antiqua). With two main languages and a large number of different fonts from two font-families, it is not possible to achieve high OCR accuracy with models pre-trained on different materials. A research group at the NLF has worked on re-OCRing this corpus and they have trained OCR models using the open-source software Tesseract, but only for the Finnish Blackletter part of the corpus. They report high accuracy results (97.64% on character level) for Finnish Blackletter but also slow performance. For the Antiqua part of the corpus, they reportedly use Tesseract's pre-trained Antiqua model, but they do not report any accuracy results. Also, they have still not published any work done on the material written in Swedish. In this work, we have explored methods and practices for training high-accuracy OCR models that can be used for efficiently recognizing the entire corpus of historical Finnish newspapers and journals. We selected 13,000 Finnish and 11,000 Swedish text lines from the corpus, of which half are printed in Blackletter and half in Antiqua fonts. After transcribing these lines, we used them for training and testing OCR models with two open-source OCR tool-kits, Ocropy and Calamari. We performed experiments with different training data setups, along with different neural network configurations and architectures. Furthermore, we tested how the voting mechanism behaves with different OCR models. Post-correction can further improve the final OCR results, especially in cases when the text, due to material damage or ink bleed, is incomprehensible without a broader context. Therefore, we have also explored different post-correction methods and implemented one of them. We compared the method's effect on OCR results of different accuracy. The biggest accomplishment of this work is succeeding in training a high-accuracy model that is capable of recognizing both Finnish and Swedish text, as well as Blackletter and Antiqua fonts. Having a mixed model for all the data and not needing to separately perform language or font identification is extremely practical when dealing with such a large corpus. Furthermore, we found that the results improve when voting with five mixed models, resulting in accuracy between 97.2% and 98.4% on the character level, which is up to 11% better than the current ABBYY results. Finally, the post-correction experiments showed that, even with a simple automatic method, post-correction can further improve OCR results. Depending on the starting OCR accuracy, the post-correction improved accuracy between 0.1-0.4%, which is a relative improvement of 0.9-12.5%.

This article presents the semantic portal and Linked Open Data service WARVICTIMSAMPO 1914-1922 about the war victims, battles, and prisoner camps in the Finnish Civil and other wars in 1914-1922. The system is based on a database of the National Archives of Finland and additional related data created, compiled, and linked during the project. The system contains detailed information about some 40,000 deaths extracted from several data sources and data about over 1,000 battles of the Civil War. A key novelty of WARVICTIMSAMPO 1914-1922 is the integration of ready-to-use Digital Humanities visualizations and data analysis tooling with semantic faceted search and data exploration, which allows, e.g., studying data about wider prosopographical groups in addition to individual war victims. The article focuses on demonstrating how the tools of the portal, as well as the underlying SPARQL endpoint openly available on the Web, can be used to explore and analyze war history in flexible and visual ways. WARVICTIMSAMPO 1914-1922 is a new member in the series of "Sampo" model-based semantic portals. The portal is in use and has had 23,000 users, including both war historians and the general public seeking information about their deceased relatives.