Transforming the Generative Pretrained Transformer Into Augmented Business Text Writer

This study uses transformers architecture of Artificial neural networks to generate artificial business text for a given topic or theme. The implication of the study is to augment the business report writing, and general business writings process with help of Generative pretrained transformers (GPT) networks. Main focus of study is to provide practical use case for GPTs models with help of big data. Our study model has 355 million model parameters and trained for three months on GPU enable devices using 2.3 billion text tokens(is available as open-source data now). Text tokens are collected with help of rigorous preprocessing, which includes; shortlisting of Subreddits of Fortune 500 companies and industries, listed on US-based social news aggregation online portal called "Reddit". After shortlisting, millions of submission of users during the five years, are parsed to collect the URLs out of it. 1.8 million working URLs are scrutinized. Business text is parsed, cleaned, and converted into word embeddings out of URLs. The result shows that both models; conditional interactive and random sampling, generate text paragraphs that are grammatically accurate and stick to the given topic.

Discovering Dominant Users’ Opinions in Reddit

Reddit is a social news aggregation and discussion website. Users submit content to the site such as links to news, which are then voted up or down by other members who in turn, can comment on others’ posts to continue the conversation. In this work, we are interested in modeling how users interact with each other in Reddit debates, to discover the most dominant opinions in a debate. To this end, we introduce a user-based model for analysis of Reddit debates. In this model, comments by users are grouped per user, describing their opinion in relation to the root comment of the debate, and users are represented with a single node in a weighted graph, where node’s weights represent relevance of user’s opinions and edges represent agreement or disagreement relationships between users throughout the debate. In this model, agreement or disagreement between the opinions of two users is defined by aggregating the set of single interactions that have occurred between them during the debate. In this work we present a skeptical aggregation model for this task. For measuring the relevance of user’s opinions, we consider two models: one based on the score of all the user’s comments and other based on the user’s karma, as computed by the Reddit platform. We characterize the set of most dominant opinions with an argumentative-based model, using the information of disagreement between opinions and relevance of opinions.

INCELS ON REDDIT: A STUDY IN SOCIAL NORMS AND DECENTRALISED MODERATION

The social news website Reddit has a long history of hosting communities (‘subreddits’) that advocate or encourage white supremacy (Gillespie 2018), disparagement of minority groups (Topinka 2017), and violence against women (Massanari 2017). As a platform that relies heavily on volunteer moderators to self-govern the subreddits (Matias 2016), Reddit has been criticised for failing to adequately enforce its site-wide rules (Gillespie 2018). Incels—an internet subculture that ascribes to deeply misogynistic beliefs—grew in visibility when they developed subreddits on Reddit. After ongoing criticism and media attention about harmful behaviour of incels both on and off the platform, Reddit imposed escalating sanctions and ultimately banned the most visible of these subreddits over a period of several years. In this paper, we focus on the interaction between formal rules and social norms in incel and related subreddits. This paper aims to improve understanding about how problematic norms are contested in (partially-) decentralised systems of content moderation. We examine discourse about moderation to better understand the role of moderation teams in maintaining and changing social norms in their communities and to examine the interaction between these norms and both sitewide and subreddit-specific rules. Our analysis suggests that the threat of prohibition alone is unlikely to be sufficient to drive cultural change in problematic subreddits. We argue that content moderation is an insufficient frame to understand the regulation of harmful communities; real change requires addressing the underlying cultural norms rather than focusing on individual pieces of content.

Social News Use & Citizen Participation among Young Activists in Singapore

This article presents a study of how civically engaged young adults engage with news on social media, within the context of a developing democracy – Singapore. Based on in-depth interviews with 20 young activists, it discusses how they approach social media as a source of news, what motivates them to engage in more than one social news platform, and how social news use fits into their political lexicon. The results reveal that despite their affinity towards news-related content on social media, they are neither partial towards mainstream, nor alternative news providers on this medium. Their primary social news platform is perceived to offer the best means to disseminate news-related information. However, they are also concerned about their privacy and practice certain strategies to mitigate this. Despite its drawbacks, the activists accept social news use as a viable means of political socialisation and mobilisation.

Information Agenda on Entertainment Radio Stations during the COVID19 Pandemic on the Example of “Radio Dacha”

The coronavirus pandemic has had a significant impact on the Russian media, which, regardless of their format, have been broadcasting news about the infection since the beginning of the outbreak on a regular basis. The main purpose of the research is to establish a connection between the epidemiological situation in Russia during the first wave and the nature of the change in the information agenda on the air of the entertainment “Radio Dacha”. To reach this goal the author, using the method of inclusive observation, analyzes 1 219 episodes of the news program, aired from January 2020, when radio hosts first mentioned the COVID-19, to July 2020, when the main restrictions were lifted in Russia. The article shows that depending on the epidemiological state, the number of notes about the coronavirus increased. The maximum quantity of news about the disease was noted in April and May when the country had the highest amount of cases and announced a lockdown. Meanwhile, the content analysis indicated that there was direct and indirect news coverage of the coronavirus. Their ratio is 97 to 3 % in favor of direct news. It means that despite the format of the radio station, the radio hosts only talked about political and social news with little or no entertain ment content. Thus, the epidemic dynamic changed the information agenda, and the worst it was, the more news about the coronavirus went on the air. The news about the infection became the longest discussed subject on the air of “Radio Dacha”.

Media Promosi Gudnyus.id Berbasis Motion Graphic

Gudnyus.id is an online media and social news website concept. To support its popularity and bring out its existence, a promotional medium is needed. Based on the questionnaire that the author has distributed online via google form with the target of general public respondents in the urban center of Batam, information is obtained from the questionnaire, 60.6% of the public does not know while 39.4% knows, but only sees Gudnyus. Id posters or flyers on social media. Consequently, Gudnyus.id requires promotional support in the form of video, which is chosen because Gudnyus.id has no promotional support in the form of videos, particularly animated videos. In the production of promotional videos, the author uses the Villamil-Molina search method to support the design. There are five phases: development, pre-production, production, postproduction and delivery. The author uses the epic model method as an analysis to test the efficacy of Gudnyus.id's promotional media based on motion graphs. The information in the promotional video includes an introduction to Gudnyus.id as an online media, website heading, and the social media used by Gudnyus.id to transmit the information. from the output of the efficacy analysis using epic model, it’s known that the promotional video of Gudnyus.id based on the movement charts was found to be effective with an average value of 4.05 empathy, 4.26 persuasion, 4.0 impact, 4.22 Communication.

Read it on Reddit: Homogeneity and Ideological Segregation in the Age of Social News

This article empirically revisits the idea of ideological segregation and homogeneity in social networks with an exhaustive analysis of the website Reddit.com . Using a computer-assisted analysis on a corpus of multiple billion comments, it studies the relation between the tone of comments and three political topics, immigration, macroeconomics and defense. Looking at the standard deviation of the average tone of users and communities over time on these specific topics, results show an overall trend toward more ideological heterogeneity as a product of new users’ influx, while multiple communities studied and long-term users show a trend toward homogeneity. Results also show that digital heuristics, such as upvotes and downvotes, illustrate a far greater diversity of opinion than a study solely on published comments could let on.

The Institutional Impacts of Algorithmic Distribution: Facebook and the Australian News Media

Since changing its algorithm in January 2018 to boost the content of family and friends over other content (including news), Facebook has signaled that it is less interested in news. However, the field is still trying to understand the long-term impacts of this change for news publishers. This is a problem because policymakers and legislators across the world are becoming concerned about the relationship between platforms and publishers. In particular, there are worries that platforms’ ability to make unilateral decisions about how their algorithms operate may harm the economic sustainability of journalism. This article provides some clarity around the relationship between these two parties through a longitudinal study of the Australian news media sector’s relationship with Facebook from 2014 to 2020, with a particular focus on the January 2018 algorithm change. We do this by analyzing Facebook data (2,082,804 posts from CrowdTangle) and external traffic data from 32 major Australian news outlets. These data are contextualized by additional desk research. We identify a range of trends including the decline of news sharing, the collapse in the performance of “social news,” the variable position of social media as a source of referral traffic, and, most critically, the diffused nature of the 2018 algorithm change. Our approach cannot make direct causal inferences. We can only identify trends in on-platform performance and referral traffic, which we then contextualize with industry reportage. However, the data provide vital longitudinal insights into the performance and responses of individual media outlets, news categories, and the Australian media sector as a whole during a critical moment of algorithmic change.