What can Analysis of the Organizations’ Web Sites tell us about AI? Comparative Study of the Online Resources operated at Google, Yandex, and Baidu

The current social and cultural debates on AI and how it is being embedded into the reality of social life have reignited scientific debates on how to study AI, what counts as data, and the conditions under which information and data pertaining AI turn into knowledge. In this paper the authors’ focus was exploring new sources of data on AI and methods of AI phenomena examination. The paper presents the results of a comparative analysis of Google, Yandex, and Baidu’s websites. Contrary to these companies commonly being perceived as online search engines, Google, Baidu, and Yandex have multiple offerings across mobile products and services, knowledge products, translation services, open platforms for startups, PC client software and AI technologies. In the first part of the paper the authors compare information presented on these companies’ websites about their goals, their technologies, how they define AI, the proclaimed social problems associated with using AI, and the forms of interaction between these companies and their audiences. The second part of the paper analyzes 20 projects that won the Google AI Impact Challenge contest. Analyzing these projects allowed for identifying areas of application of AI technologies inside and outside organizations, for characterizing AI’s potential roles as a mediator in relations between people, and finally for highlighting utopian and dystopian scenarios associated with implementing AI in social relations. In the conclusion the authors formulate a set of broader questions for social analytics concerning artificial intelligence grounded in the results of their analysis.

Anbar: Collection and analysis of a large scale Urdu language Twitter corpus

The confluence of high performance computing algorithms and large scale high-quality data has led to the availability of cutting edge tools in computational linguistics. However, these state-of-the-art tools are available only for the major languages of the world. The preparation of large scale high-quality corpora for low resource language such as Urdu is a challenging task as it requires huge computational and human resources. In this paper, we build and analyze a large scale Urdu language Twitter corpus Anbar. For this purpose, we collect 106.9 million Urdu tweets posted by 1.69 million users during one year (September 2018-August 2019). Our corpus consists of tweets with a rich vocabulary of 3.8 million unique tokens along with 58K hashtags and 62K URLs. Moreover, it contains 75.9 million (71.0%) retweets and 847K geotagged tweets. Furthermore, we examine Anbar using a variety of metrics like temporal frequency of tweets, vocabulary size, geo-location, user characteristics, and entities distribution. To the best of our knowledge, this is the largest repository of Urdu language tweets for the NLP research community which can be used for Natural Language Understanding (NLU), social analytics, and fake news detection.

On the Development of an Information System for Monitoring User Opinion and its Role for the Public

Social media services and analytics platforms are rapidly growing. A large number of various events happen mostly every day, and the role of social media monitoring tools is also increasing. Social networks are widely used for managing and promoting brands and different services. Thus, most popular social analytics platforms aim for business purposes while monitoring various social, economic, and political problems remains underrepresented and not covered by thorough research. Moreover, most of them focus on resource-rich languages such as the English language, whereas texts and comments in other low-resource languages such as the Russian and Kazakh languages in social media are not represented well enough. So, this work is devoted to developing and applying the information system called the OMSystem for analyzing users’ opinions on news portals, blogs, and social networks in Kazakhstan. The system uses sentiment dictionaries of the Russian and Kazakh languages and machine learning algorithms to determine the sentiment of social media texts. The whole structure and functionalities of the system are also presented. In the experimental part, the system’s monitoring of the healthcare, political and social aspects of the most relevant topics connected with the vaccination against the coronavirus disease are thoroughly observed and analyzed. The analysis allowed discovering the public social mood in the cities of Almaty and Nur-Sultan and other large regional cities of Kazakhstan. The system’s study included two extensive periods: 10-01-2021 to 30-05-2021 and 01-07-2021 to 12-08-2021. In the obtained results, people’s mood and attitude to the Government’s policies and actions were studied by such social network indicators as the level of topic discussion activity in society, the level of interest in the topic in society, and the mood level of society. These indicators calculated by the OMSystem allowed careful identification of alarming factors of the public (negative attitude to the government regulations, vaccination policies, trust to vaccination, etc.) and assessment of the social mood.

Social Analytics Through Spyral

In this paper we introduce Spyral, a notebook environment that works in tandem with Voyant Tools. Voyant Tools is an online digital text analysis environment. Spyral notebooks extend Voyant and allow users to create JavaScript online notebooks with both text and code cells. These notebooks are designed to allow for collaboration between scholars. We also discuss both the possibilities and some limitations of the notebook environment. We provide examples of both tutorial notebooks that can be used for pedagogical purposes as well as examples of Spyral notebooks used in collaborative analysis. Finally, we suggest potential areas for further development for Spyral.

Wellbeing and Worldbuilding: The Science Fiction of Wellbeing Measurement

Wellbeing has evolved into a fragmented, contextual, and multi-dimensional construct, underpinned by numerous measurement methodologies at many different scales. To explore wellbeing through science fiction and fantasy (SFF), we don’t just need SFF about health and happiness. We also need SFF about measurement: SFF that explores surveillance, data infrastructures, the political economy of personal data, ‘the metric gaze,’ social analytics, and the social life of metrics. This chapter explores just a few linkages between wellbeing, measurement, and SFF, mainly emphasising dystopian fiction. Has SFF struggled to imagine wellbeing policy — or any holistic stance on the myriad factors that inform the happiness and flourishing of populations — except where the interested party is some sinister elite? How might SFF's narratives of distant planets and more-than-human intelligences help us to formulate a more inclusive understanding of the wellbeing of the many morally weighty beings and worlds that inhabit this planet?

Motif-h: a novel functional backbone extraction for directed networks

Dense networks are very pervasive in social analytics, biometrics, communication, architecture, etc. Analyzing and visualizing such large-scale networks are significant challenges, which are generally met by reducing the redundancy on the level of nodes or edges. Motifs, patterns of the higher order organization compared with nodes and edges, are recently found to be the novel fundamental unit structures of complex networks. In this work, we proposed a novel motif h-backbone (Motif-h) method to extract functional cores of directed networks based on both motif strength and h-bridge. Compared with the state-of-the-art method Motif-DF and Entropy, our method solves two main issues which are often found in existing methods: the Motif-h reconsiders weak ties into our candidate set, and those weak ties often have critical functions of bridges in networks; moreover, our method provides a trade-off between the motif size and the edge strength, which quantifies the core edges accordingly. In the simulations, we compare our method with Motif-DF in four real-world networks and found that Motif-h can streamline the extraction of crucial structures compared with the others with limited edges.

An Enterprise Social Analytics Dashboard to Support Competence Valorization and Diversity Management

This paper describes an Enterprise Social Analytics Dashboard (ESAD) to support human capital management, competence valorization, diversity management, and early detection of potential problems within large, networked organizations. The system can be used by managers for career promotion, team building, and diversity management, as well as by company’s social analysts, to monitor social behaviors and information flow in the workplace. Toward this end, we defined a measure of informal leadership which draws on organization theory and on a computational model based on multiplex networks. This model, along with a social network analysis toolkit developed in the context of the present study, enabled the systematic empirical analysis of social behaviors in a three-year dataset of message threads exchanged within a large multinational enterprise, as a function of gender, time, roles, and discussed topics. The results of our empirical analysis demonstrate the power of social analytics in organizations as a tool for human capital management, competence valorization, and early detection of potential problems. Our study clearly shows that Enterprise Social Networks are a favorable environment to highlight women’s leadership qualities and intermediary abilities. The ESAD offers innovative features, such as a sociologically motivated leadership model based on multiplex networks, text mining, and text classification techniques, to extract relevant discussion topics.

INTERIOR AND EXTERIOR DRIVERS IN THE SYSTEM OF CONFESSIONAL DYNAMICS

Статья рассматривает разные аспекты процессов конфессиональной динамики на примере российского лютеранства, от предреволюционного периода до сегодняшнего дня. В системных процессах конфессиональной динамики выделяются две большие группы факторов: экстериорные и интериорные. К экстериорным относится прежде всего региональная, географическая миграция, приводящая к наполнению и демографическому перераспределению физического и социального пространства. Из интериорных факторов наиболее подробно рассматриваются процессы самоидентификации, которые, в свою очередь, сочетаются с религиозной идентификацией, для чего используется идентификационная модель, предъявленная обществу и закрепленная в социальной памяти. Отсутствие или размывание этой модели приводит к изменениям личной идентичности или затруднениям в ее определении. Сдвиги в коллективной идентичности современного российского лютеранства (фактические - например, ритуальные, или декларативные) ведут к идентфикационным сдвигам индивидуального плана. Прогностические возможности социальной аналитики в сфере религии подразумевают навыки системного анализа религии и религиозной ситуации. The paper examines various aspects of confessional dynamics through the history of Russian Lutheran Church from the prerevolutionary period to the present. The systemic processes of confessional dynamics rest on two major groups of factors: exterior and interior. Regional migration is the primary exterior factor bringing about the populating and demographic reshaping of physical and social fields. Processes of self-identification combined with religious identification are examined as the most influential interior factors. The identificational pattern is embedded in society and social memory. Personal identification can be diverted or complicated in absence or dilution of such a pattern. Diversions in the collective identity of the contemporary Russian Lutheran congregation (such as ritual or declarative) lead to identity diversions on an individual level. Prognostic potential of social analytics in religious sphere involves systemic analysis of religion and religious situation.