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This study is motivated to provide a theoretically informed, data-driven assessment of the consequences associated with the participation of non-human bots in social accountability movements; specifically, the anti-inequality/anti-corporate #OccupyWallStreet conversation stream on Twitter.

A latent Dirichlet allocation (LDA) topic modeling approach as well as XGBoost machine learning algorithms are applied to a dataset of 9.2 million #OccupyWallStreet tweets in order to analyze not only how the speech patterns of bots differ from other participants but also how bot participation impacts the trajectory of the aggregate social accountability conversation stream. The authors consider two research questions: (1) do bots speak differently than non-bots and (2) does bot participation influence the conversation stream.

The results indicate that bots do speak differently than non-bots and that bots exert both weak form and strong form influence. Bots also steadily become more prevalent. At the same time, the results show that bots also learn from and adapt their speaking patterns to emphasize the topics that are important to non-bots and that non-bots continue to speak about their initial topics.

These findings help improve understanding of the consequences of bot participation within social media-based democratic dialogic processes. The analyses also raise important questions about the increasing importance of apparently nonhuman actors within different spheres of social life.

The current study is the first, to the authors’ knowledge, that uses a theoretically informed Big Data approach to simultaneously consider the micro details and aggregate consequences of bot participation within social media-based dialogic social accountability processes.

The authors investigate how COVID-19 has influenced the amount, type or topics of abuse that UK politicians receive when engaging with the public.

This work covers the first year of COVID-19 in the UK, from March 2020 to March 2021 and analyses Twitter abuse in replies to UK MPs. The authors collected and analysed 17.9 million reply tweets to the MPs. The authors present overall abuse levels during different key moments of the pandemic, analysing reactions to MPs by gender and the relationship between online abuse and topics such as Brexit, the government’s COVID-19 response and policies, and social issues.

The authors have found that abuse levels towards UK MPs were at an all-time high in December 2020. Women (particularly those from non-White backgrounds) receive unusual amounts of abuse, targeting their credibility and capacity to do their jobs. Similar to other large events like general elections and Brexit, COVID-19 has elevated abuse levels, at least temporarily.

Previous studies analysed abuse levels towards MPs in the run-up to the 2017 and 2019 UK General Elections and during the first four months of the COVID-19 pandemic in the UK. The authors compare previous findings with those of the first year of COVID-19, as the pandemic persisted, and Brexit was forthcoming. This research not only contributes to the longitudinal comparison of abuse trends against UK politicians but also presents new findings, corroborates, further clarifies and raises questions about the previous findings.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-07-2022-0392

This study identified how investor sentiment on Twitter is associated with Brazilian stock market return and trading volume.

The study analyzes 314,864 tweets between January 1, 2017, to December 31, 2018, collected with the Tweepy library. The companies’ financial data were obtained from Refinitiv Eikon. Using the netnographic method, a Twitter Investor Sentiment Index (ISI) was constructed based on terms associated with the stocks. This Twitter sentiment was attributed through machine learning using the Google Cloud Natural Language API. The associations between Twitter sentiment and market performance were performed using quantile regressions and vector auto-regression (VAR) models, because the variables of interest are heterogeneous and non-normal, even as relationships can be dynamic.

In the contemporary period, the ISI is positively correlated with stock market returns, but negatively correlated with trading volume. The autoregressive analysis did not confirm the expectation of a dynamic relationship between sentiment and market variables. The quantile analysis showed that the ISI explains the stock market return, however, only at times of lower returns. It is possible to state that this effect is due to the informational content of the tweets (sentiment), and not to the volume of tweets.

The study presents unprecedented evidence for the Brazilian market that investor sentiment can be identified on Twitter, and that this sentiment can be useful for the formation of an investment strategy, especially in times of lower returns. These findings are original and relevant to market agents, such as investors, managers and regulators, as they can be used to obtain abnormal returns.

Purpose: The purpose of this paper is to consolidate knowledge and benchmark the progress being made across the 32 International Federations (IFs) in the Summer Olympic Programme.

Design/methodology/approach: A website content analysis, analytical hierarchy of information, and social media research was conducted to triangulate the barriers and drivers of environmental sustainability (ES) progress. This data was then analysed to empirically substantiate the findings of previous methods by exploring potential drivers of IF ES progress and communication and refining the ranking of IF ES progress.

Results and findings: World Sailing is by far the most advanced IF in terms of ES progress, followed by World Athletics. Only 4 out of 32 have any sort of strategic ES plans. Only golf, surfing, football, sailing, and hockey have received any academic attention. There is a significant lack of understanding of environmental practices across sport, and their drivers/barriers. There is limited accountability with regards to ES progress and activities throughout the Olympic Movement. This has resulted in uneven diffusion of environmental activities.

Originality: This paper is a new contribution to sport management and ES literature. It provides a benchmark of understanding for ES in the Summer Olympic Programme for the first time using a hierarchy of information to ground results. The exploration and comparison of the perspectives of separate sports adds to the paper's originality.

Though the circular economy (CE) is a current buzzword, this still lacks a precise definition. In the absence of a clear notion of what that term includes, actions taken by the government and companies may not be well informed. In particular, those actions need to consider what people mean when people talk about the CE, either to refocus people's decisions or to undertake a more effective communications strategy.

Since people voice people's opinions mainly through social media nowadays, special attention has to be paid to discussions on those media. In this paper, the authors focus on Twitter as a popular social platform to deliver one's thoughts quickly and fast. The authors' research aim is to get the perceptions of people about the CE. After collecting more than 100,000 tweets over 16 weeks, the authors analyse those tweets to understand the public discussion about the CE. The authors conduct a frequency analysis of the most recurring words, including the words' association with other words in the same context and categorise them into a set of topics.

The authors show that the discussion focuses on the usage of resources and materials that heavily endanger sustainability, i.e. carbon and plastic and the harmful habit of wasting. On the other hand, the two most common good practices associated with the CE and sustainability emerge as recycling and reuse (the latter being mentioned far less). Also, the business side of the CE appears to be relevant.

The outcome of this analysis can drive suitable communication strategies by which companies and governments interested in the development of the CE can understand what is actually discussed on social media and call for the attention.

This paper addresses the lack of a standard definition the authors highlighted in the Introduction. The results confirm that people understand CE by looking both at CE's constituent activities and CE's expected consequences, namely the reduction of waste, the transition to a green economy free of plastic and other pollutants and the improvement of the world climate.

This paper investigates circular economy communications and stakeholder dialogic engagement with circular economy posts published by European agri-food companies on Twitter from the spread of the COVID-19 pandemic. It explores the use of social media as a dialogic tool to activate circular economy engagement in order to involve all supply-chain actors on the route to a circular transition.

A coding framework based on the reclassification of the Glossary of Circular Economy, according to a 4-R paradigm (reduce, reuse, recycle and recover), was developed for the analysis. All tweets published by a sample of European agri-food companies, starting from the start of the COVID-19 pandemic until data extraction, were collected, purified and analysed.

Agri-food companies showed a higher level of engagement through social media, even if mainly focused on “recycling” and “general circular economy” issues. In general, awareness among social network users of the need to be part of the circular economy transition emerged. Moreover, the highest percentage of posts published by the companies' Twitter accounts was informative rather than interactive. In addition, starting with the COVID-19 pandemic crisis, the circular economy has arisen as a central topic of debate and a driver for the rethinking process of the agri-food business community.

To the best of the authors' knowledge, this research represents the first study focused on circular economy engagement through social media from the company perspective in the agri-food industry.

Automatically extracting stance information from natural language texts is a significant research problem with various applications, particularly after the recent explosion of data on the internet via platforms like social media sites. Stance detection system helps determine whether the author agree, against or has a neutral opinion with the given target. Most of the research in stance detection focuses on the English language, while few research was conducted on the Arabic language.

This paper aimed to address stance detection on Arabic tweets by building and comparing different stance detection models using four transformers, namely: Araelectra, MARBERT, AraBERT and Qarib. Using different weights for these transformers, the authors performed extensive experiments fine-tuning the task of stance detection Arabic tweets with the four different transformers.

The results showed that the AraBERT model learned better than the other three models with a 70% F1 score followed by the Qarib model with a 68% F1 score.

A limitation of this study is the imbalanced dataset and the limited availability of annotated datasets of SD in Arabic.

Provide comprehensive overview of the current resources for stance detection in the literature, including datasets and machine learning methods used. Therefore, the authors examined the models to analyze and comprehend the obtained findings in order to make recommendations for the best performance models for the stance detection task.

High levels of child obesity alongside rising stunting and the absence of a coherent food policy have deemed UK’s food system to be broken. The National Food Strategy (NFS) was debated intensely in media, with discussions on how and who should fix the food system.

Using a mixed methods approach, the authors conduct framing analysis on traditional media and sentiment analysis of twitter reactions to the NFS to identify frames used to shape food system policy interventions.

The study finds evidence that the media coverage of the NFS often utilised the tropes of “culture wars” shaping the debate of who is responsible to fix the food system – the government, the public or the industry. NFS recommendations were portrayed as issues of free choice to shift the debate away from government action correcting for market failure. In contrast, the industry was showcased as equipped to intervene on its own accord. Dietary recommendations made by the NFS were depicted as hurting the poor, painting a picture of helplessness and loss of control, while their voices were omitted and not represented in traditional media.

British media’s alignment with free market economic thinking has implications for food systems reform, as it deters the government from acting and relies on the invisible hand of the market to fix the system. Media firms should move beyond tropes of culture wars to discuss interventions that reform the structural causes of the UK’s broken food systems.

As traditional media coverage struggles to capture the diversity of public perception; the authors supplement framing analysis with sentiment analysis of Twitter data. To the best of our knowledge, no such media (and social media) analysis of the NFS has been conducted. The paper is also original as it extends our understanding of how media alignment with free market economic thinking has implications for food systems reform, as it deters the government from acting and relies on the invisible hand of the market to fix the system.

This study aims to explore which outcome measures are used by occupational therapy staff in adult social care settings in the UK, and the factors affecting use of outcome measures.

A quantitative descriptive research design was used, using a cross-sectional study to explore occupational therapy staff views on the use of outcome measures. A 38-question survey was developed on Microsoft Forms. Recruitment occurred online over a three-week period in 2021 via the social media platform “Twitter”. Results were analysed using Excel using descriptive statistics and qualitative results used thematic analysis.

Participants (n = 20) used a range of outcome measures (13) in adult social care settings in the previous 12 months. Standardised measures were used by half the sample in the previous 12 months. The Therapy Outcome Measure and Barthel Index were in most use. The breadth of adult social care practice and practical factors such as caseload and lack of a meaningful tool were found to be barriers to outcome measure use. Facilitators included service improvement, accountability, use of audit and professional occupational therapy leadership.

The overall use of outcome measures can be considered low in this setting, with manager support seen to be key to the use of outcome measures. Further research is needed to investigate nationwide use.

Training, time and manager support are key to use of standardised tests and outcome measures in the adult social care settings. The use of occupational performance measures should be considered to demonstrate unique professional impact.

This contemporary study reveals use of outcome measures within occupational therapy adult social care services in the UK, which is an under researched and under published area.

Stock market has always been lucrative for various investors. But, because of its speculative nature, it is difficult to predict the price movement. Investors have been using both fundamental and technical analysis to predict the prices. Fundamental analysis helps to study structured data of the company. Technical analysis helps to study price trends, and with the increasing and easy availability of unstructured data have made it important to study the market sentiment. Market sentiment has a major impact on the prices in short run. Hence, the purpose is to understand the market sentiment timely and effectively.

The research includes text mining and then creating various models for classification. The accuracy of these models is checked using confusion matrix.

Out of the six machine learning techniques used to create the classification model, kernel support vector machine gave the highest accuracy of 68%. This model can be now used to analyse the tweets, news and various other unstructured data to predict the price movement.

This study will help investors classify a news or a tweet into “positive”, “negative” or “neutral” quickly and determine the stock price trends.