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We investigate how bots influence human-to-human interaction in online communities. In doing so, we distinguish between reflexive and supervisory bots delegated by community participants and moderators, respectively. We hypothesize that reflexive bot activity will reduce direct reciprocity and increase generalized reciprocity and that supervisory bot activity will reduce preferential attachment among human participants. Through an analysis of almost 70 million posts on the discussion communities on Reddit, a popular platform for online discussions, we found support for the hypotheses. [ABSTRACT FROM AUTHOR]

Incentivized blockchain-based online social media (BOSM), where creators and curators of popular content are paid in cryptocurrency, have recently emerged. Traditional social media ecosystems have experienced significant bot involvement in their platforms, which has often had a negative impact on both users and platforms. BOSM can provide additional direct financial incentives as motivation for both bots' and human users' engagement. Using the panel vector autoregression and regression discontinuity in time framework, we analyze two distinct data sets from Steemit, the largest and most popular BOSM, to study the impact of bot engagement on human users and the impact of changes in financial reward on user engagement. Interestingly, our findings demonstrate that while increased engagement by bots is positively associated with engagement by human users, the association between bot engagement and human user engagement decreases as the number of votes for a post increases. We also find that shifts in economic incentives significantly influence the behavior of both human users and bots. This research provides significant insights on how social media platforms can leverage economic incentives to influence user behavior and, more importantly, leverage bots' activity to increase the engagement of their human users. [ABSTRACT FROM AUTHOR]

Robotised propaganda has become a new stage in the dissemination of manipulative content online, exploited for its low entry costs and high effectiveness. State actors, non-state actors, and politically and business-focused actors can spread ideas, share thoughts or sow half-truths and lies with the help of hundreds of automatised accounts and contribute to the manipulation of people's minds. In a short time, robotised propaganda gained the attention of academics as well as international organisations. Especially, Russian influence operations utilising the newest advantages of technological development stand at the epicentre of research interests in the Central and Eastern European region. In our research, we aim to test the hypothesis that information from media which are presented as pro-Russian were connected to a higher percentage of accounts classified as bots. The research sample covers all the media categories in the Czech Republic, as stated by the Political Capital study 'The Weaponization of Culture: Kremlin's traditional agenda and the export of values to Central Europe'. For robotised accounts activity analysis, we use a script based on a tweetbotornot R package by Kearney. As the results show, pro-Russian positive media have a statistically significant higher probability for bot account activity than media expressing neutral opinions. [ABSTRACT FROM AUTHOR]

The dissemination of disinformation has become a formidable weapon, with nation-states exploiting social media platforms to engineer narratives favorable to their geopolitical interests. This study delved into Russia's orchestrated disinformation campaign, in three times periods of the 2022 Russian-Ukraine War: its incursion, its midpoint and the Ukrainian Kherson counteroffensive. This period is marked by a sophisticated blend of bot-driven strategies to mold online discourse. Utilizing a dataset derived from Twitter, the research examines how Russia leveraged automated agents to advance its political narrative, shedding light on the global implications of such digital warfare and the swift emergence of counter-narratives to thwart the disinformation campaign. This paper introduces a methodological framework that adopts a multiple-analysis model approach, initially harnessing unsupervised learning techniques, with TweetBERT for topic modeling, to dissect disinformation dissemination within the dataset. Utilizing Moral Foundation Theory and the BEND Framework, this paper dissects social-cyber interactions in maneuver warfare, thereby understanding the evolution of bot tactics employed by Russia and its counterparts within the Russian-Ukraine crisis. The findings highlight the instrumental role of bots in amplifying political narratives and manipulating public opinion, with distinct strategies in narrative and community maneuvers identified through the BEND framework. Moral Foundation Theory reveals how moral justifications were embedded in these narratives, showcasing the complexity of digital propaganda and its impact on public perception and geopolitical dynamics. The study shows how pro-Russian bots were used to foster a narrative of protection and necessity, thereby seeking to legitimize Russia's actions in Ukraine whilst degrading both NATO and Ukraine's actions. Simultaneously, the study explores the resilient counter-narratives of pro-Ukraine forces, revealing their strategic use of social media platforms to counteract Russian disinformation, foster global solidarity, and uphold democratic narratives. These efforts highlight the emerging role of social media as a digital battleground for narrative supremacy, where both sides leverage information warfare tactics to sway public opinion. [ABSTRACT FROM AUTHOR]

To detect bots in online survey data, there is a wealth of literature on statistical detection using only responses to Likert-type items. There are two traditions in the literature. One tradition requires labeled data, forgoing strong model assumptions. The other tradition requires a measurement model, forgoing collection of labeled data. In the present article, we consider the problem where neither requirement is available, for an inventory that has the same number of Likert-type categories for all items. We propose a bot detection algorithm that is both model-agnostic and unsupervised. Our proposed algorithm involves a permutation test with leave-one-out calculations of outlier statistics. For each respondent, it outputs a p value for the null hypothesis that the respondent is a bot. Such an algorithm offers nominal sensitivity calibration that is robust to the bot response distribution. In a simulation study, we found our proposed algorithm to improve upon naive alternatives in terms of 95% sensitivity calibration and, in many scenarios, in terms of classification accuracy. [ABSTRACT FROM AUTHOR]

Internet recruitment methods for research are rapidly evolving as technology and participant preferences do as well. This brings data security concerns, balanced with respect to persons for research participants. Internet recruitment research strategies are still important given the importance of creating private and accessible pathways for potentially marginalized populations or people experiencing stigmatized mental health conditions to participate in research. This manuscript describes the case of social media recruitment for a mental health and racism study in Fall 2022 that was infiltrated by bots. We contextualize it in the context of data quality literature, with considerations for future directions including strategies for study design and management, and further research questions. [ABSTRACT FROM AUTHOR]

In this study, we explore the future of work by examining differences in productivity when teams are composed of only humans or both humans and machine agents. Our objective was to characterize the similarities and differences between human and human–machine teams as they work to coordinate across their specialized roles. This form of research is increasingly important given that machine agents are becoming commonplace in sociotechnical systems and playing a more active role in collaborative work. One particular class of machine agents, bots, is being introduced to these systems to facilitate both taskwork and teamwork. We investigated the association between bots and productivity outcomes in open source software development through an analysis of hundreds of project teams. The presence of bots in teams was associated with higher levels of productivity and higher work centralization in addition to greater amounts of observed communication. The adoption of bots in software teams may have tradeoffs, in that doing so may increase productivity, but could also increase workload. We discuss the theoretical and practical implications of these findings for advancing human–machine teaming research. This research characterizes the changing landscape of collaborative work brought on by the increasing presence of autonomous machine agents in teams. We report on a multidimensional analysis of work in software development projects examining differences between human‐only and human‐bot teams. [ABSTRACT FROM AUTHOR]

Distinguishing between web traffic generated by bots and humans is an important task in the evaluation of online marketing campaigns. One of the main challenges is related to only partial availability of the performance metrics: although some users can be unambiguously classified as bots, the correct label is uncertain in many cases. This calls for the use of classifiers capable of explaining their decisions. This paper demonstrates two such mechanisms based on features carefully engineered from web logs. The first is a man-made rule-based system. The second is a hierarchical model that first performs clustering and next classification using human-centred, interpretable methods. The stability of the proposed methods is analyzed and a minimal set of features that convey the class-discriminating information is selected. The proposed data processing and analysis methodology are successfully applied to real-world data sets from online publishers.

The world has been witnessing a war in Ukraine since Russia started its attacks on 24 February 2022. At this point, Russia has not only deployed its troops but also put its Twitter bots to work. The war has both covered conventional spaces and unconventional spaces like social media. In this regard, our study aims to understand to what extent bots were used by Russia during the first eight days of the war. To this end, we collect data between the 24th of February and the 4th of March. On data collection, we carry out a bot prediction for collected tweets. Findings suggest that Twitter reacted to bots quite quickly and that Russia could not manage to exploit the Russian Twitter sphere as much as it did before. Therefore, Russia chose to block Twitter.

Highlights Online surveys provide many benefits for researchers but are vulnerable to fraudulent responders that threaten data quality. We aimed to evaluate the effectiveness of current detection strategies after noting patterns in our data. We systematically reviewed responses (N = 180) from parenting studies utilizing two online platforms. Respondents were categorized as accepted (n = 45) or fraudulent (n = 135), and responses on standardized measures were examined to determine if fraudulent scores differed. Accepted participants were predominately female (98.4%) and White (67.9%). Average caregiver age was 40.61 (SD = 7.37) on Qualtrics and 59.88 (SD = 6.01) on REDCap. The majority of fraudulent responders reported being female (62.1%) and White (80.6%), with average caregiver ages of 37.83 (SD = 7.35) for Qualtrics and 56.16 (SD = 8.86) for REDCap. Fraudulent responders had significantly higher median scores on measures of caregiver distress (rDASS = 0.21) and child behavioural problems (rCBCL = 0.33). Previously recommended strategies were insufficient at preventing and identifying fraudulent responders. Recommendations for improving these strategies are discussed. The current paper includes experiences using multiple strategies to detect fraudulent responders in two online parenting studies. Improvements to previously recommended strategies and an example review form are included to aid researchers in detecting fraudulent responders. [ABSTRACT FROM AUTHOR]

“Bots,” computer software capable of taking surveys for an operator, pose a serious threat to the integrity of research that relies on publicly available online surveys. This paper addresses the issue of bot responses to online surveys and suggests several strategies for reducing and addressing these fraudulent responses. To combat this threat, researchers should employ specific methods for building, distributing, and processing surveys that deter and eliminate bot responses from the dataset. Methods for anti-bot survey design include building bot detection software into the survey, creating trap questions, and writing questions that require specific freeform answers. Survey distribution methods that avoid or hide monetary incentives, use a password-protected link, or employ some other form of population targeting will also receive fewer bot responses. Finally, data should be screened for bots after collection using a set of reliable criteria to identify and remove bot responses. [ABSTRACT FROM AUTHOR]

The rapid increase in China's outward digital presence on western social media platforms highlights China's priorities for promoting pro-Chinese narratives and stories in recent years. Simultaneously, China has increasingly been accused of launching information operations using bot activity, puppet accounts, and other inauthentic activity to amplify its messaging. This paper provides a comprehensive network analysis characterization of the hashtag influence campaign China promoted against the US-hosted Summit on Democracy in December 2021, in addition to methods to identify different types of actors within this type of influence campaign. China uses layers of state-sponsored accounts, bots, and non-bot accounts to promote its messaging. Lastly, we describe how China uses localized campaigns under a more extensive umbrella campaign for information diffusion toward targeted audiences. [ABSTRACT FROM AUTHOR]

The world has been witnessing a war in Ukraine since Russia started its attacks on 24 February 2022. At this point, Russia has not only deployed its troops but also put its Twitter bots to work. The war has both covered conventional spaces and unconventional spaces like social media. In this regard, our study aims to understand to what extent bots were used by Russia during the first eight days of the war. To this end, we collect data between the 24th of February and the 4th of March. On data collection, we carry out a bot prediction for collected tweets. Findings suggest that Twitter reacted to bots quite quickly and that Russia could not manage to exploit the Russian Twitter sphere as much as it did before. Therefore, Russia chose to block Twitter. [ABSTRACT FROM AUTHOR]

As concerns about social bots online increase, studies have attempted to explore the discourse they produce, and its effects on individuals and the public at large. We argue that the common reliance on aggregated scores of binary classifiers for bot detection may have yielded biased or inaccurate results. To test this possibility, we systematically compare the differences between non-bots and bots using binary and non-binary classifiers (classified into the categories of astroturf, self-declared, spammers, fake followers, and Other). We use two Twitter corpora, about COVID-19 vaccines (N = 1,697,280) and climate change (N = 1,062,522). We find that both in terms of volume and thematic content, the use of binary classifiers may hinder, distort, or mask differences between humans and bots, that could only be discerned when observing specific bot types. We discuss the theoretical and practical implications of these findings. [ABSTRACT FROM AUTHOR]

Social networking platforms have become one of the most engaging portals on the Internet, enabling global users to express views, share news and campaigns, or simply exchange information. Yet there is an increasing number of fake and spam profiles spreading and disseminating fake information. There have been several conscious attempts to determine and distinguish genuine news from fake campaigns, which spread malicious disinformation among social network users. Manual verification of the huge volume of posts and news disseminated via social media is not feasible and humanly impossible. To overcome the issue, this research presents a framework to use sentiment analysis based on emotions to investigate news, posts, and opinions on social media. The proposed model computes the sentiment score of content-based entities to detect fake or spam and detect Bot accounts. The authors also present an investigation of fake news campaigns and their impact using a machine learning algorithm with highly accurate results as compared to other similar methods. The results presented an accuracy of 99.68 %, which is significantly higher as compared to other methodologies delivering lower accuracy.

In this non-empirical article, which is intended as a decision-making resource for researchers, we identify issues surrounding data integrity that commonly arise in online survey research and we propose remediation strategies based on challenges we encountered during a particular pilot study as well as our collective experience with conducting online survey research. Using the metaphor of an onion, we peel off the layers of this complex problem, synthesize the various available strategies used across disciplines, and propose some novel ones based on our perspective as psychologists. Corresponding to this multi-layered problem, we propose multi-layered solutions to prevent illegitimate responding—by both humans and non-humans (robots or “bots” for short)—from compromising the quality of data collected via online survey research. The first layer entails strategic item selection and protective programming in survey development. The second layer involves astute advertising and recruitment tactics to minimize illegitimate responses during survey dissemination. The third layer includes algorithms and other mechanisms to identify suspicious responses for possible exclusion during data verification. When we peel off the layers and reach the core problem of illegitimate responses to online surveys—financial incentives—we will propose ways of navigating respondent reimbursement to mitigate their inadvertent harmful impacts on the research process. By proposing these solutions, we aim to protect the integrity of scientific inquiry in psychology, especially given how often this method is used in the discipline.

This article examines YouTubers' 'bot like' behaviour on Twitter and conceptualises it as a defiance of platform power in delimiting the boundaries of 'authenticity'. This entrepreneurial capture of 'botness' is understudied and deserves attention. We focus on a platform with a clear monetisation scheme, YouTube, and on patterns of 'inauthentic' behaviour in how people shared YouTube videos on Twitter during the early stages of the COVID-19 pandemic (February–May 2020). The global coronavirus crisis forced social media platforms to take unprecedented (mostly automated) steps to moderate content and to introduce new policies on 'appropriate' conduct, which we argue may have an impact on emerging social media content creators' attempts to boost visibility online. [ABSTRACT FROM AUTHOR]

Unlike the traditional governments, the neoliberal regime is fed by psychopolitics based on datacentered techniques. As a psychopolitical technique, psychometrics, which performs a profiling function, marks the critical stage in the transformation of the subject into a predictable object. Psychographic data provides personalized content production. Dark ads, which is produced based on psychometric data and instrumentalized in an area that extends from directing consumer behavior to affecting voter behavior, is one of these contents. The public sphere of the age, which has the ower to directly affect the voter behavior, which is seen as one of the shortest ways of mass control, turns into the field of activity of psychopolitical marketing. Simultaneously, the decision-making process is manipulated through dark advertisements based on hyper-personalization. Users are kept in streams of artificial information that are mutually exclusive by being imprisoned in fake agendas set up in accordance with their personal data. Healthy voting behavior is undermined by preventing the voters, who are placed in the position of marketing objects by psychometric calculations, from accompanying the political process freely. In this study, which focuses on the effect of psychopolitical marketing on voters, semi-structured interview technique was used. The findings led to the conclusion that the participants did not act consciously about the consumption of dark advertisements based on psychographic data and the false truth derived from the artificial agenda based on algorithms, and preferred to be exposed to such outputs instead of staying away from the aforementioned content. It is possible to make suggestions regarding the necessity of acquiring media literacy skills and social media usage skills. for the people of the age. [ABSTRACT FROM AUTHOR]

Allegheny woodrats (Neotoma magister) are karst-specializing rodents that are rare or in conservation need in many states within their current range. Parasitism and habitat fragmentation have been suggested as primary reasons for declining populations. The presence, prevalence, and impact of ectoparasites, including fleas, ticks, and bots, is not fully understood rangewide. We collected Allegheny woodrat ectoparasites across 8 states in their range, identifying parasites via morphological and genetic means. Across contributions from 8 states, we discovered 2 woodrat-specific fleas parasitizing Allegheny woodrats: Orchopeas pennsylvanicus (all contributing states, n ¼ 228) and Epitedia cavernicola (Indiana only, n ¼ 9). The former was a new state record in New Jersey and Ohio. Woodrat specialists Ixodes woodi were morphologically identified as the dominant tick species (n ¼ 38), and our contributions to genetic databases may ease confusion in future efforts. Three generalist species of ticks representing 8 individuals were identified as Dermacentor variabilis, Amblyomma americanum, and Ixodes scapularis. Only 2 bot fly species were recognized in Allegheny woodrats: 1 squirrel bot (Cuterebra emasculator) and 10 individuals of Cuterebra sp. not genetically conspecific to any known eastern U.S. rodent bot. The host specificity for fleas is not surprising, given that previous small-scale surveys and ticks primarily appear to be a mix of genusspecific (Ixodes woodi) and generalist species. There remains uncertainty with bots via morphological and genetic analyses. Our survey presents a wide-ranging baseline survey for Allegheny woodrats across their range, emphasizing the diversity (or specificity) of parasite groups for this species. An understanding of Allegheny woodrats and the health impact of ectoparasites is imperative because they face myriad challenges rangewide, especially considering the bot-driven demise of 1 woodrat in our study. Ectoparasites can have a marked impact on already-declining woodrat populations across their range and should not be overlooked in future surveys. [ABSTRACT FROM AUTHOR]

Introduction: A new artificial intelligence (AI) methodology is analyzed, with the understanding that communication is one of the most important fields of work for its application. In addition to the content collection and production phases, other areas within the world of communication such as distribution, and specifically the moderation of comments (on social networks and in the media) are also experiencing a period of innovation, but in a less obvious way for the audience. Methodology: We proceeded to find out how various AI tools can measure the quality of the conversation and combat toxicity in communicative spaces. We analyzed 43,165 tweets published from 18 to 24 October 2021 corresponding to seven Spanish politicians and the cascade of user responses. Results: The main consequences point to insults as the predominant toxic category in the comments, regardless of ideology. In addition, the conversations have an average of 21% of bots. Discussion: Given the above, this research shows how new AI methodologies can account for a hitherto qualitative term such as toxicity and contradicts previous findings on bots as spreaders of toxicity, with real users generating the most toxicity. Conclusions: In the specific study of politics, there is a perceived difference in behaviors between horizontal conversation between peers and vertical conversation with politicians. Therefore, these tools help to make visible new realities such as toxicity, with the ultimate aim of eradicating it and cleaning up the online debate. [ABSTRACT FROM AUTHOR]

The disinformation techniques are evolving alongside with the evolution of artificial intelligence. The creating and dissemination of disinformation is more effective especially due to perpetrators using deepfake as convincing illusion of reality, bots as artificial means of making disinformation relevant and psychographic microtargeting as powerful tool for tailoring perfect disinformation for each individual. European Union is aware of the dangers imposed by artificial intelligence, but the question is whether current or proposed regulations have the potential to tackle the issue effectively. [ABSTRACT FROM AUTHOR]

This study explores the transformative potential of Generative AI (GenAI) and ChatBots in educational interaction, communication, and the broader implications of human-GenAI collaboration. By examining the related literature through data mining and analytical methods, the paper identifies three main research themes: the revolutionary role of GenAI-powered ChatBots in educational interactions, their capability to enrich social learning, and their dual role as both support and assistance within educational settings. This research further highlights the impact of human-GenAI interaction in education from social, psychological, and cultural perspectives, focusing on social presence as a fundamental component of the teaching and learning process. It discusses the integration of GenAI and ChatBots into education and considers whether this marks the dawn of an algorithmic renaissance that elevates educational experiences or an apocalypse that threatens the very essence of human learning and interaction. [ABSTRACT FROM AUTHOR]

Una de las transformaciones más recientes en el ámbito de la comunicación digital ha sido la impulsada por la introducción de la inteligencia artificial y la automatización. La comunicación política ha incorporado estas tecnologías a algunos de sus procesos y tareas, alterando la forma de relacionarse con los públicos, de producir contenidos y de poner en circulación los mensajes. Con todo, la utilización de estos sistemas también introduce riesgos y desafíos para la democracia y la esfera pública. El objetivo de este artículo es analizar la investigación científica internacional centrada en el uso de la inteligencia artificial y la automatización en el ámbito de la comunicación política. Se aboga por una perspectiva holística a fin de profundizar en las características de la producción científica y en las particularidades de esta comunicación política algorítmica. Para alcanzar este propósito se parte de un diseño metodológico basado en una revisión sistematizada de la literatura, aplicando un enfoque tanto cuantitativo como cualitativo. Los resultados constatan que se trata de un campo incipiente que destaca por una evolución irregular, el trabajo colaborativo entre investigadores, la producción en inglés, las aproximaciones cualitativas y el interés por el estudio del impacto de los bots, la propaganda computacional y la manipulación de la opinión pública. Además, se identifican dos ejes temáticos centrales: usos y características de la comunicación política mediada por inteligencia artificial; y amenazas y desafíos que se derivan del uso de estas tecnologías.

Abstract As digitalization increases, countries employ digital diplomacy, harnessing digital resources to project their desired image. Digital diplomacy also encompasses the interactivity of digital platforms, providing a trove of public opinion that diplomatic agents can collect. Social media bots actively participate in political events through influencing political communication and purporting coordinated narratives to influence human behavior. This article provides a methodology towards identifying three types of bots: General Bots, News Bots and Bridging Bots, then further identify these classes of bots on Twitter during a diplomatic incident involving the United States and China. In the balloon incident that occurred in early 2023, where a balloon believed to have originated from China is spotted across the US airspace. Both countries have differing opinions on the function and eventual handling of the balloon. Using a series of computational methods, this article examines the impact of bots on the topics disseminated, the influence and the use of information maneuvers of bots within the social communication network. Among others, our results observe that all three types of bots are present across the two countries; bots geotagged to the US are generally concerned with the balloon location while those geotagged to China discussed topics related to escalating tensions; and perform different extent of positive narrative and network information maneuvers. The broader implications of our work towards policy making is the systematic identification of the type of bot users and their properties across country lines, enabling the evaluation of how automated agents are being deployed to disseminate narratives and the nature of narratives propagated, and therefore reflects the image that the country is being projected as on social media; as well as the perception of political issues by social media users.

Despite increased empirical attention, theory on bots and how they act to disseminate information on social media remains poorly understood. Our study leverages the conduit brokerage perspective and the findings of a multiple case study to develop a novel framework of algorithmic conduit brokerage for understanding information dissemination by bots and the design choices that may influence their actions. Algorithmic conduit brokerage encompasses two intertwined processes. The first process, algorithmic social alertness, relies on bot activity to curate and reconfigure information. Algorithmic social alertness is significant because it involves action triggers that dictate the kinds of information being searched, discovered, and retrieved by bots. The second process, algorithmic social transmission, relies on bot activity to embellish and distribute the information curated. Algorithmic social transmission is important because it can broaden the reach of information disseminated by bots through increased discoverability and directed targeting. The two algorithmic conduit brokerage processes we offer are unique to bots and distinct from the original conceptualization of conduit brokerage, which is rooted in human activity. First, since bots lack the human ability of sensemaking and are instead fueled by automation and action triggers rather than by emotions, algorithmic conduit brokerage is more invariant and reliable than human conduit brokerage. Second, automation increases the speed and scale of information curation and transfer, making algorithmic conduit brokerage not only more consistent but also faster and more extensive. Third, algorithmic conduit brokerage includes a set of new concepts (e.g., action triggers and rapid scaling) that are specific to bots and therefore not applicable to human conduit brokerage. [ABSTRACT FROM AUTHOR]

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Modern Software Engineering thrives with innovative tools that aid developers in creating better software grounded on quality standards. Software bots are an emerging and exciting trend in this regard, supporting numerous software development activities. As an emerging trend, few studies describe and analyze different bots in software development. This research presents a systematic literature review covering the state of the art of applied and proposed bots for software development. Our study spans literature from 2003 to 2022, with 82 different bots applied in software development activities, covering 83 primary studies. We found four bot archetypes: chatbots which focus on direct communication with developers to aid them, analysis bots that display helpful information in different tasks, repair bots for resolving software defects, and development bots that combine aspects of other bot technologies to provide a service to the developer. The primary benefits of using bots are increasing software quality, providing useful information to developers, and saving time through the partial or total automation of development activities. However, drawbacks are reported, including limited effectiveness in task completion, high coupling to third-party technologies, and some prejudice from developers toward bots and their contributions. We discovered that including Bots in software development is a promising field of research in software engineering that has yet to be fully explored. [ABSTRACT FROM AUTHOR]

As digitalization increases, countries employ digital diplomacy, harnessing digital resources to project their desired image. Digital diplomacy also encompasses the interactivity of digital platforms, providing a trove of public opinion that diplomatic agents can collect. Social media bots actively participate in political events through influencing political communication and purporting coordinated narratives to influence human behavior. This article provides a methodology towards identifying three types of bots: General Bots, News Bots and Bridging Bots, then further identify these classes of bots on Twitter during a diplomatic incident involving the United States and China. In the balloon incident that occurred in early 2023, where a balloon believed to have originated from China is spotted across the US airspace. Both countries have differing opinions on the function and eventual handling of the balloon. Using a series of computational methods, this article examines the impact of bots on the topics disseminated, the influence and the use of information maneuvers of bots within the social communication network. Among others, our results observe that all three types of bots are present across the two countries; bots geotagged to the US are generally concerned with the balloon location while those geotagged to China discussed topics related to escalating tensions; and perform different extent of positive narrative and network information maneuvers. The broader implications of our work towards policy making is the systematic identification of the type of bot users and their properties across country lines, enabling the evaluation of how automated agents are being deployed to disseminate narratives and the nature of narratives propagated, and therefore reflects the image that the country is being projected as on social media; as well as the perception of political issues by social media users. [ABSTRACT FROM AUTHOR]

The fragmentation of the Spanish party system has led to a growing political polarization, particularly evident on the Internet. The discursive strategies of political actors online, combined with the particularities of communication on social media platforms such as the disinhibition effect, echo chambers, and filter bubbles, are likely to promote a state of tension among digital citizens. This tension is particularly pronounced in gender issues, which have become key positions of political parties, with convergence of sexism in the community and a highly reactive misogynistic online culture that turns female politicians into easy targets. Starting from the hashtag #IreneMonteroDimision, which conveys a paradigmatic criticism movement against the Spanish Minister of Equality, an analysis of the social conversation on Twitter is developed to determine its intentionality, tone, and orientation, as well as the themes and users that generated the most tension. In this way, multimodal content and discourse analysis is applied to the 418 tweets with the greatest interaction and explicitly directed at the minister. The hypothesis is that the criticism of Irene Montero goes beyond the management of her Ministry to place her at the center of a power struggle in which political ideology, hate speech, and antifeminism converge. The study has shown that attacks on Montero extend to the Executive and the feminist movement; they are promoted by a mass of politicized and polarized profiles, with constant activism and a tendency towards contagious replication of messages. These practices, as well as the participation of artificial profiles, allow us to appreciate signs of astroturfing; behind this seemingly natural critical reaction, there are orchestrated movements of antifeminist and far-right profiles (gender trolling), but also of trans-exclusionary feminists opposed to legislation promoted by Montero. [ABSTRACT FROM AUTHOR]

Unstructured pathology report plays a major role in definitive cancer diagnosis. Accessing or searching unstructured textual information from the clinical pathology reports is one of the major concerns in cancer healthcare sector to provide precise medicine, analysis of cancer outcomes, providing cancer care services, accurate measurement for future prediction, treatment history, and comparative future research work. An efficient methodology has to be introduced for to extract quantitative information from the unstructured cancer data. Integrating computational intelligence in Robotic Process Automation can be done to process this data and automate repetitive activities for evaluating patients clinical pathology report. RPA-based NLP BERT system is designed and evaluated to automatically extract information on these variables for the patients from pathology report. In order to detect tumour and outcomes from documented pathology reports, a supervised machine learning keyword based extraction algorithm was developed in which the pathology data are examined to extract keywords from 2087 reports with 1579 of data reports being processed for the development phase and 508 of data being used for evaluation. The precision recall and accuracy are calculated for organ specimens for cancer test as (0.984, 0.982, 0.9839), test methodology(0.986, 0.981,0.9956) and pathological result(0.986, 0.9938, 0.9795) were achieved. The feasibility of autonomously extracting pre-defined data from clinical narratives for cancer research were established in this work. The outcomes showed that our methodology was suitable for actual use in obtaining essential information from pathology reports. [ABSTRACT FROM AUTHOR]

The contemporary info-proliferation is taking the ideal of a solid technological rationalism to its extreme point: the depletion of all bodies into 'informational cuts', orderable bits and pieces of data fabric. The present contribution will discuss this process of datafication, trying to avoid any polarization along the 'pro' or 'anti' dualism, and any consequent excess of enthusiasm or critique. For this purpose, the essay will take the form of a stroll across post-digital culture, alternatively under the effects of a 'red and a blue pill' as the two main points of view already exemplified, in 1999, by Morpheus in the famous sci-fi movie The Matrix. To these two points of view, respectively identifiable as digital critique (going down the deep rabbit hole, and seeing that computers are playing today a leading role in what Gilles Deleuze and Fèlix Guattari have called 'capitalist schizophrenia'), or digital potential (remaining in a world of numeric dreams, a world populated not only by humans but also by bots, autonomous computer programs that are becoming increasingly able not only to post, but also to understand content and interact with people and, most importantly, to take aesth/ethical decisions), a yellow one will be added, which can be recognized as that of 'hacker culture', at the same time suggesting that, instead of a dialectical contraposition between two different perceptual and cognitive modalities, post-digital culture can be more easily discussed through a multiplication of possible perspectives. [ABSTRACT FROM AUTHOR]