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Selected Papers from the 9th Annual Conference "Comparative Media Studies in Today's World" (CMSTW'2021)

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A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Techno-Social Smart Systems".

Deadline for manuscript submissions: closed (15 October 2021) | Viewed by 19260

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Special Issue Editor

Prof. Dr. Svetlana S. Bodrunova

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Guest Editor

School of Journalism and Mass Communications, St. Petersburg State University, 199004 St. Petersburg, Russia
Interests: social media; Twitter; YouTube; big data; automated text analysis; topic modelling; public sphere; journalism; comparative media studies; social communication; Russian media and society

Special Issue Information

Dear Colleagues,

This Special Issue of Future Internet features the best papers from the 9^th annual conference of ‘Comparative Media Studies in Today’s World’ (CMSTW’2021), which was held from April 20 to 21, 2021, in St. Petersburg, Russia (virtually).

The scope of the conference was exploring the interplay between communication architectures and other features of online life. Recently, scholars have proclaimed the rise of the platform society. In it, affordances decide what message the medium is; algorithmic intermediaries, commercial corporations, and influencer bloggers compete in bypassing news agencies and platforms impeach politicians. The reshaping of power orders, the renewal of the public/private debate, context-bound differences, and the transformation of discussion practices all pose the question of what we are facing in the emergent communication architecture(s) of our life: is there a horizontal co-existence of communication platforms, a multi-level complex of arenas, or a brave new world of (re-)emergent hierarchies?

User talk is a major part of online communication today and may be seen as both the contents of platforms’ ‘communication vessels’ and as a part of the communication architecture itself. The authors of this Special Issue explore how features of the communication structure and context relate to the content and discursive features of today’s (and future) Internet discussions.

Prof. Dr. Svetlana Bodrunova
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Future Internet is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

online media
online discussions
communication architectures
networked discussions
platforms
social media
social networks

Published Papers (6 papers)

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Editorial

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3 pages, 175 KiB

Open AccessEditorial

Editorial for the Special Issue “Selected Papers from the 9th Annual Conference ‘Comparative Media Studies in Today’s World’ (CMSTW’2021)”

by Svetlana S. Bodrunova

Future Internet 2022, 14(11), 334; https://doi.org/10.3390/fi14110334 - 16 Nov 2022

Viewed by 988

Abstract

This Special Issue of Future Internet features the best papers from the 9th annual conference “Comparative Media Studies in Today’s World (CMSTW’2021)”, which was held between 20 and 21 April 2021, in St [...] Full article

(This article belongs to the Special Issue Selected Papers from the 9th Annual Conference "Comparative Media Studies in Today's World" (CMSTW'2021))

Research

Jump to: Editorial

25 pages, 2453 KiB

Open AccessArticle

Transformer-Based Abstractive Summarization for Reddit and Twitter: Single Posts vs. Comment Pools in Three Languages

by Ivan S. Blekanov, Nikita Tarasov and Svetlana S. Bodrunova

Future Internet 2022, 14(3), 69; https://doi.org/10.3390/fi14030069 - 23 Feb 2022

Cited by 10 | Viewed by 5916

Abstract

Abstractive summarization is a technique that allows for extracting condensed meanings from long texts, with a variety of potential practical applications. Nonetheless, today’s abstractive summarization research is limited to testing the models on various types of data, which brings only marginal improvements and does not lead to massive practical employment of the method. In particular, abstractive summarization is not used for social media research, where it would be very useful for opinion and topic mining due to the complications that social media data create for other methods of textual analysis. Of all social media, Reddit is most frequently used for testing new neural models of text summarization on large-scale datasets in English, without further testing on real-world smaller-size data in various languages or from various other platforms. Moreover, for social media, summarizing pools of texts (one-author posts, comment threads, discussion cascades, etc.) may bring crucial results relevant for social studies, which have not yet been tested. However, the existing methods of abstractive summarization are not fine-tuned for social media data and have next-to-never been applied to data from platforms beyond Reddit, nor for comments or non-English user texts. We address these research gaps by fine-tuning the newest Transformer-based neural network models LongFormer and T5 and testing them against BART, and on real-world data from Reddit, with improvements of up to 2%. Then, we apply the best model (fine-tuned T5) to pools of comments from Reddit and assess the similarity of post and comment summarizations. Further, to overcome the 500-token limitation of T5 for analyzing social media pools that are usually bigger, we apply LongFormer Large and T5 Large to pools of tweets from a large-scale discussion on the Charlie Hebdo massacre in three languages and prove that pool summarizations may be used for detecting micro-shifts in agendas of networked discussions. Our results show, however, that additional learning is definitely needed for German and French, as the results for these languages are non-satisfactory, and more fine-tuning is needed even in English for Twitter data. Thus, we show that a ‘one-for-all’ neural-network summarization model is still impossible to reach, while fine-tuning for platform affordances works well. We also show that fine-tuned T5 works best for small-scale social media data, but LongFormer is helpful for larger-scale pool summarizations. Full article

(This article belongs to the Special Issue Selected Papers from the 9th Annual Conference "Comparative Media Studies in Today's World" (CMSTW'2021))

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12 pages, 574 KiB

Open AccessArticle

Phubber’s Emotional Activations: The Association between PANAS and Phubbing Behavior

by Andrea Guazzini, Tommaso Raimondi, Benedetta Biagini, Franco Bagnoli and Mirko Duradoni

Future Internet 2021, 13(12), 311; https://doi.org/10.3390/fi13120311 - 4 Dec 2021

Cited by 14 | Viewed by 4241

Abstract

Currently, mobile phones are widely used worldwide. Thus, phubbing rapidly became a common phenomenon in our social life. Phubbing is considered by the literature as a new form of technology-related addiction that may undermine interpersonal relationships and mental health. Our study contributed to exploring phubbers’ emotional activation as no other work has investigated it so far. Indeed, researchers have only explored phubbees’ but not phubbers’ emotional correlates. A sample of 419 Italian individuals (143 males) participated in our data collection on a voluntary basis. The results showed that phubbing is related to negative affects, but not to positive affects. Moreover, phubbing in both its components (i.e., communication disturbance, phone obsession) appeared to elicit an emotional activation similar to that of social media addiction. These findings may help in strengthening the discussion around the emotional consequences of virtual environment design, as well as the awareness about what happens at a relational level during phubbing. Full article

(This article belongs to the Special Issue Selected Papers from the 9th Annual Conference "Comparative Media Studies in Today's World" (CMSTW'2021))

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13 pages, 8476 KiB

Open AccessArticle

Detection of Induced Activity in Social Networks: Model and Methodology

by Dmitrii Gavra, Ksenia Namyatova and Lidia Vitkova

Future Internet 2021, 13(11), 297; https://doi.org/10.3390/fi13110297 - 22 Nov 2021

Cited by 4 | Viewed by 2291

Abstract

This paper examines the problem of social media special operations and especially induced support in social media during political election campaigns. The theoretical background of the paper is based on the study fake activity in social networks during pre-election processes and the existing models and methods of detection of such activity. The article proposes a methodology for identifying and diagnosing induced support for a political project. The methodology includes a model of induced activity, an algorithm for segmenting the audience of a political project, and a technique for detecting and diagnosing induced support. The proposed methodology provides identification of network combatants, participants of social media special operations, influencing public opinion in the interests of a political project. The methodology can be used to raise awareness of the electorate, the public, and civil society in general about the presence of artificial activity on the page of a political project. Full article

(This article belongs to the Special Issue Selected Papers from the 9th Annual Conference "Comparative Media Studies in Today's World" (CMSTW'2021))

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11 pages, 801 KiB

Open AccessArticle

Community Formation as a Byproduct of a Recommendation System: A Simulation Model for Bubble Formation in Social Media

by Franco Bagnoli, Guido de Bonfioli Cavalcabo’, Banedetto Casu and Andrea Guazzini

Future Internet 2021, 13(11), 296; https://doi.org/10.3390/fi13110296 - 22 Nov 2021

Cited by 3 | Viewed by 2258

Abstract

We investigate the problem of the formation of communities of users that selectively exchange messages among them in a simulated environment. This closed community can be seen as the prototype of the bubble effect, i.e., the isolation of individuals from other communities. We develop a computational model of a society, where each individual is represented as a simple neural network (a perceptron), under the influence of a recommendation system that honestly forward messages (posts) to other individuals that in the past appreciated previous messages from the sender, i.e., that showed a certain degree of affinity. This dynamical affinity database determines the interaction network. We start from a set of individuals with random preferences (factors), so that at the beginning, there is no community structure at all. We show that the simple effect of the recommendation system is not sufficient to induce the isolation of communities, even when the database of user–user affinity is based on a small sample of initial messages, subject to small-sampling fluctuations. On the contrary, when the simulated individuals evolve their internal factors accordingly with the received messages, communities can emerge. This emergence is stronger the slower the evolution of individuals, while immediate convergence favors to the breakdown of the system in smaller communities. In any case, the final communities are strongly dependent on the sequence of messages, since one can get different final communities starting from the same initial distribution of users’ factors, changing only the order of users emitting messages. In other words, the main outcome of our investigation is that the bubble formation depends on users’ evolution and is strongly dependent on early interactions. Full article

(This article belongs to the Special Issue Selected Papers from the 9th Annual Conference "Comparative Media Studies in Today's World" (CMSTW'2021))

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17 pages, 594 KiB

Open AccessArticle

Detection of Hidden Communities in Twitter Discussions of Varying Volumes

by Ivan Blekanov, Svetlana S. Bodrunova and Askar Akhmetov

Future Internet 2021, 13(11), 295; https://doi.org/10.3390/fi13110295 - 20 Nov 2021

Cited by 9 | Viewed by 2466

Abstract

The community-based structure of communication on social networking sites has long been a focus of scholarly attention. However, the problem of discovery and description of hidden communities, including defining the proper level of user aggregation, remains an important problem not yet resolved. Studies of online communities have clear social implications, as they allow for assessment of preference-based user grouping and the detection of socially hazardous groups. The aim of this study is to comparatively assess the algorithms that effectively analyze large user networks and extract hidden user communities from them. The results we have obtained show the most suitable algorithms for Twitter datasets of different volumes (dozen thousands, hundred thousands, and millions of tweets). We show that the Infomap and Leiden algorithms provide for the best results overall, and we advise testing a combination of these algorithms for detecting discursive communities based on user traits or views. We also show that the generalized K-means algorithm does not apply to big datasets, while a range of other algorithms tend to prioritize the detection of just one big community instead of many that would mirror the reality better. For isolating overlapping communities, the GANXiS algorithm should be used, while OSLOM is not advised. Full article

(This article belongs to the Special Issue Selected Papers from the 9th Annual Conference "Comparative Media Studies in Today's World" (CMSTW'2021))

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