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Social network sites have been widely adopted by politicians in the last election campaigns. To increase the effectiveness of these campaigns the potential electorate is to be identified, as targeted ads are much more effective than non-targeted ads. Therefore, the purpose of this paper is to propose and implement a new methodology for automatic prediction of political orientation of users on social network sites by comparison to texts from the overtly political parties’ pages.

To this end, textual information on personal users’ pages is used as a source of statistical features. The authors apply automatic text categorization algorithms to distinguish between texts of users from different political wings. However, these algorithms require a set of manually labeled texts for training, which is typically unavailable in real life situations. To overcome this limitation the authors propose to use texts available on various political parties’ pages on a social network site to train the classifier. The political leaning of these texts is determined by the political affiliation of the corresponding parties. The classifier learned on such overtly political texts is then applied on the personal user pages to predict their political orientation. To assess the validity and effectiveness of the proposed methodology two corpora were constructed: personal Facebook pages of 450 Israeli citizens, and political parties Facebook pages of the nine prominent Israeli parties.

The authors found that when a political tendency classifier is trained and tested on data in the same corpus, accuracy is very high. More significantly, training on manifestly political texts (political party Facebook pages) yields classifiers which can be used to classify non-political personal Facebook pages with fair accuracy.

Previous studies have shown that targeted ads are more effective than non-targeted ads leading to substantial saving in the advertising budget. Therefore, the approach for automatic determining the political orientation of users on social network sites might be adopted for targeting political messages, especially during election campaigns.

This paper proposes and implements a new approach for automatic cross-corpora identification of political bias of user profiles on social network. This suggests that individuals’ political tendencies can be identified without recourse to any tagged personal data. In addition, the authors use learned classifiers to determine which self-identified centrists lean left or right and which voters are likely to switch allegiance in subsequent elections.

Reliability and political bias of mass media has been a controversial topic in the literature. The purpose of this paper is to propose and implement a methodology for fully automatic evaluation of the political tendency of the written media on the web, which does not rely on subjective human judgments.

The underlying idea is to base the evaluation on fully automatic comparison of the texts of articles on different news websites to the overtly political texts with known political orientation. The authors also apply an alternative approach for evaluation of political tendency based on wisdom of the crowds.

The authors found that the learnt classifier can accurately distinguish between self-declared left and right news sites. Furthermore, news sites’ political tendencies can be identified by automatic classifier learnt from manifestly political texts without recourse to any manually tagged data. The authors also show a high correlation between readers’ perception (as a “wisdom of crowds” evaluation) of the bias and the classifier results for different news sites.

The results are quite promising and can put an end to the never ending dispute on the reliability and bias of the press.

This paper proposes and implements a new approach for fully automatic (independent of human opinion/assessment) identification of political bias of news sites by their texts.

User profiling with big data raises significant issues regarding privacy. Privacy studies typically focus on individual privacy; however, in the era of big data analytics, users are also targeted as members of specific groups, thus challenging their collective privacy with unidentified implications. Overall, this paper aims to argue that in the age of big data, there is a need to consider the collective aspects of privacy as well and to develop new ways of calculating privacy risks and identify privacy threats that emerge.

Focusing on a collective level, the authors conducted an extensive literature review related to information privacy and concepts of social identity. They also examined numerous automated data-driven profiling techniques analyzing at the same time the involved privacy issues for groups.

This paper identifies privacy threats for collective entities that stem from data-driven profiling, and it argues that privacy-preserving mechanisms are required to protect the privacy interests of groups as entities, independently of the interests of their individual members. Moreover, this paper concludes that collective privacy threats may be different from threats for individuals when they are not members of a group.

Although research evidence indicates that in the age of big data privacy as a collective issue is becoming increasingly important, the pluralist character of privacy has not yet been adequately explored. This paper contributes to filling this gap and provides new insights with regard to threats for group privacy and their impact on collective entities and society.

Incorporating DuBois's concept of a racial “double-consciousness” and extending Foucault's work on the Panopticon, I examine current day racial profiling processes and the effects of hyper-surveillance on communities of color. DuBois suggests that the citizen of color has a sense of duality based upon minority status and being an American. This duality offers insight into the way race “works” that few Whites comprehend. Foucault argues that the permanent visibility of those subjected to the Panopticon generates awareness of the power differential between individuals and the state. The current examination is a contextualization of narratives from people of color who experience governance and surveillance via racial profiling.

Partisan news media, which often publish extremely biased, one-sided or even false news, are gaining popularity world-wide and represent a major societal issue. Due to a growing number of such media, a need for automatic detection approaches is of high demand. Automatic detection relies on various indicators (e.g. content characteristics) to identify new partisan media candidates and to predict their level of partisanship. The aim of the research is to investigate to a deeper extent whether it would be appropriate to rely on the hyperlinks as possible indicators for better automatic partisan news media detection.

The authors utilized hyperlink network analysis to study the hyperlinks of partisan and mainstream media. The dataset involved the hyperlinks of 18 mainstream media and 15 partisan media in Slovakia and Czech Republic. More than 171 million domain pairs of inbound and outbound hyperlinks of selected online news media were collected with Ahrefs tool, analyzed and visualized with Gephi software. Additionally, 300 articles covering COVID-19 from both types of media were selected for content analysis of hyperlinks to verify the reliability of quantitative analysis and to provide more detailed analysis.

The authors conclude that hyperlinks are reliable indicators of media affinity and linking patterns could contribute to partisan news detection. The authors found out that especially the incoming links with dofollow attribute to news websites are reliable indicators for assessing the type of media, as partisan media rarely receive links with dofollow attribute from mainstream media. The outgoing links are not such reliable indicators as both mainstream and partisan media link to mainstream sources similarly.

In contrast to the extensive amount of research aiming at fake news detection within a piece of text or multimedia content (e.g. news articles, social media posts), the authors shift to characterization of the whole news media. In addition, the authors did a geographical shift from more researched US-based media to so far under-researched European context, particularly Central Europe. The results and conclusions can serve as a guide how to derive new features for an automatic detection of possibly partisan news media by means of artificial intelligence (AI).

The peer review history for this article is available at the following link: https://publons.com/publon/10.1108/OIR-10-2020-0441.

The paper aims to relocate discussions on police stops and police interactions from the Anglophone world to the particularistic context of the post-colonial state of India. The paper further frames the everyday policing practices in a theoretical dialog between questions of legitimacy, accountability and tolerated illegalities. For that purpose, the author contextualizes the discussion in the post-colonial state of India, in the jurisdictions of two police stations (PSs), in the National Capital Territory of Delhi and the State of Kerala.

The author conducted ethnographic studies in one station each in Kerala and Delhi, India, from February to July 2019 and July 2019 to January 2020, respectively. The study mapped everyday power relations as the relations manifested within the site and jurisdiction of the PSs.

Through the research, the author found that to fully understand everyday practices of policing, especially police interactions and police stops, one must contextualize the police force within the administrative power-sharing relations, police force's accountability structures, legal procedures and class dynamics, which mark the terrain in which personnel function. In that terrain, the author found that the dialog between particularistic legitimacy, accountability and tolerated illegalities offered an important framework to interpret the everyday policing practices.

Through the paper, the author seeks to expand the analysis of ethnographic descriptions of policing by contextualizing them in the political economy of the state. In doing so, the author aims to provide a framework through which police interactions in post-colonial India could be understood

In the last four years, since Volume I of this Bibliography first appeared, there has been an explosion of literature in all the main functional areas of business. This wealth of material poses problems for the researcher in management studies — and, of course, for the librarian: uncovering what has been written in any one area is not an easy task. This volume aims to help the librarian and the researcher overcome some of the immediate problems of identification of material. It is an annotated bibliography of management, drawing on the wide variety of literature produced by MCB University Press. Over the last four years, MCB University Press has produced an extensive range of books and serial publications covering most of the established and many of the developing areas of management. This volume, in conjunction with Volume I, provides a guide to all the material published so far.

THE study of fatigue from a physiological aspect is a field which motion study technicians have made little or no attempt to explore. Shame on their heads. The physiological simplification of motions aimed at reducing fatigue could have completely offset the notion that motion study is aimed at converting the operator into an automaton. It may well be that an elaborate motion pattern set‐up designed to simplify the work merely succeeds in setting up stresses in the worker. The superimposing of a time‐studied standard for the job may not have taken into account the adaptation of the speed of motions to the physiological limitations of the operator working at a high level performance. Very few practitioners have attempted to study motions in the factory with a view to reducing fatigue and stress as a prerequisite to studying the set‐up for increased production. Still fewer have attempted to evaluate these factors. It is about time they did.

Qualitative information‐gathering techniques are focused on to determine whether they can be adapted or adopted to support strategic goal‐setting. Much of the literature suggests that if planning is based on information gathered and presented in a manner which managers can understand they are more likely to act on it, and, for this reason, qualitative rather than quantitative techniques are stressed here. Factors which are not amenable to numerate analysis but which are useful to the strategic planner, such as experience, judgement and intuition, are also isolated and analysed. An attempt is made to facilitate the use of qualitative data‐gathering methods and suggestions are made as to where particular techniques may prove beneficial, together with their limitations. Research, from a small (n = 20), in‐depth survey of small business owners/ managers in Canada, is included which shows that they do not use quantitative planning processes but that judgemental techniques were most widely used; in general, the less sophisticated the planning process the higher it would be ranked among the survey participants. The research from other surveys also shows that scientific mathematically based models often do not fit with small business organisational reality and that methodologies should be developed that integrate research into the decision‐making process.