Internet Postings Research Articles

Коммуникативные приемы управления цифровой репутацией органов исполнительной власти

The article considers the concept of digital reputation in relation to the executive authorities of the Russian Federation, as well as identifies the technological and communicative methods of its formation and maintenance. The digital reputation of a public administration body is a multicriterial assessment of its activities. It appears in media space and reflects the opinions of socially active population. Digital reputation is not identical to digital image. The authors revealed the following discursive factors that shape stereotypes about public administration. For instance, an indefinitely wide range of addressees lack basic knowledge in public administration; public assessments are flexible and unpredictable; some Internet users practice provocative speech behavior, etc. The research featured internet posts made by the Ministry of Science and Higher Education of the Russian Federation in the social network VKontakte. The service Popsters.ru made it possible to rate them by user involvement. A discursive and stylistic analysis of the posts with a high engagement coefficient revealed the following effective tools for managing the digital reputation of the authorities: hashtags as means of data distribution and systematization, dialogical and personalized presentation, interactive forms of communication (voting, polls, etc.), timely reaction to user comments, etc.

Sentiment analysis of internet posts on vaccination using ChatGPT and comparison with actual vaccination rates in South Korea

Background This study used ChatGPT for sentiment analysis to investigate the possible links between online sentiments and COVID-19 vaccination rates. It also examines Internet posts to understand the attitudes and reasons associated with vaccine-related opinions. Methods We collected 500,558 posts over 60 weeks from the Blind platform, mainly used by working individuals, and 854 relevant posts were analyzed. After excluding duplicates and irrelevant content, attitudes toward and reasons for vaccine opinions were studied through sentiment analysis. The study further correlated these categorized attitudes with the actual vaccination data. Results The proportions of posts expressing positive, negative, and neutral attitudes toward COVID-19 vaccines were 5%, 83%, and 12%, respectively. The total post count showed a positive correlation with the vaccination rate, indicating a high correlation between the number of negative posts about the vaccine and the vaccination rate. Negative attitudes were predominantly associated with societal distrust and perceived oppression. Conclusions This study demonstrates the interplay between public perceptions of COVID-19 vaccines as expressed through social media and vaccination behavior. These correlations can serve as useful clues for devising effective vaccination strategies.

Sentiment analysis of internet posts on vaccination using ChatGPT and comparison with actual vaccination rates in South Korea

Background This study used ChatGPT for sentiment analysis to investigate the possible links between online sentiments and COVID-19 vaccination rates. It also examines Internet posts to understand the attitudes and reasons associated with vaccine-related opinions. Methods We collected 500,558 posts over 60 weeks from the Blind platform, mainly used by working individuals, and 854 relevant posts were analyzed. After excluding duplicates and irrelevant content, attitudes toward and reasons for vaccine opinions were studied through sentiment analysis. The study further correlated these categorized attitudes with the actual vaccination data. Results The proportions of posts expressing positive, negative, and neutral attitudes toward COVID-19 vaccines were 5%, 83%, and 12%, respectively. The total post count showed a positive correlation with the vaccination rate, indicating a high correlation between the number of negative posts about the vaccine and the vaccination rate. Negative attitudes were predominantly associated with societal distrust and perceived oppression. Conclusions This study demonstrates the interplay between public perceptions of COVID-19 vaccines as expressed through social media and vaccination behavior. These correlations can serve as useful clues for devising effective vaccination strategies.

The Use of Technology in Teaching and Learning English through E-Learning for Students College

The purpose of the study is to give information to the teachers on teaching needs using technology, especially in the classroom. For now, we are faced with the Pandemic Covid-19 and teachers continue to be demanded to be more careful in choosing the right technology when teaching online. E-learning is one of the digital platforms for learning in the digital era. This type of research is the methods library research. This article is literature-based in which the data and information were gathered from books, internet postings, journals, social media, etc. Technology includes the use of materials, tools, techniques, and sources of power to make life easier. In education, Technology is really able to improve the teaching and learning process, especially in teaching English during the pandemic.

Metaphor as Invective in the Genre of Internet Commentary: A Focus on German Political Discourse

This article presents the results of a linguo-stylistic analysis of a Germanlanguage commentary within the context of public political internet discourse. The analysis aims to identify distinctive features specific to this genre. Empirical material consists of over 200 comments on internet posts made by the German political party “Alternative for Germany,” selected through random sampling. The study applies both general scientific and linguistic methods, such as discourse analysis and contextual analysis. The relevance of this chosen topic is justified by the need for research in the field of internet linguistics, particularly in addressing genre-related issues. The goal is to determine, identify, and describe the characteristic features of commentary as an internet text. The article thoroughly examines the approaches taken by German linguists to define the concept of “invective,” encompassing lexical-semantic perspectives, speech act theory, and post-structural discourse analysis. Additionally, the analysis of the material reveals that invective is a hallmark of German political internet discourse, with manifestations in both explicit and implicit forms. The article discusses implicatures, arising through irony and metaphorization. The author concludes that metaphor can serve as a tool of persuasion. Metaphor, as a persuasive device, involves the intentional alteration of the mental representation of a given object based on the metaphorical image imposed by the manipulator. This typically includes exaggeration in some aspects of the metaphorically represented phenomenon while diminishing others.

Sublethal toxicities of 2,4-dinitrophenol as inferred from online self-reports.

2,4-dinitrophenol (DNP) is a mitochondrial toxin sometimes used as a weight loss agent. Reports of fatalities from DNP have been increasing since 2000, suggesting an increase in use. Our understanding of DNP toxicity in humans comes from reports to Poison Control and postmortem analyses, sources that are biased to more extreme presentations. This leads to a gap in our knowledge about the adverse effects of DNP at nonlethal doses. Here we investigate the doses and effects of DNP as reported online. We analyzed publicly available Internet posts that we collected from 2017-2019. The posts came from anonymous users or users who voluntarily self-identified. We collected data from websites whose terms of use allow for the secondary analysis of data that their users agree to make public. We used natural language processing techniques that we had previously developed to extract doses, effects, and substances mentioned in each post. We collected 1,630 posts across 5 online forums and the Reddit forum r/DNP. The posts were from 1,234 unique usernames. The most commonly reported doses were between 150 to 300 mg each day followed by 300 to 450 mg each day. At those doses, the most reported adverse effects were profuse sweating and fatigue. Reports of thermoregulatory (sweating, feeling hot flashes or flushed), fatigue-related, and neurologically related symptoms were statistically significantly more frequent at reported daily doses greater than 150 mg than doses below 150 mg (post-hoc χ2-test with Bonferroni correction). The effects were judged as plausible by two board-certified medical toxicologists. Triiodothyronine, clenbuterol, testosterone, and trenbolone, an androgenic anabolic steroid were the most significantly co-mentioned substances. Fatigue, increased body temperature, and paresthesias from DNP are reported more frequently at doses greater than 150 mg each day than at doses less than 150 mg each day. Online discussions of DNP frequently mention androgenic anabolic steroids and other weight loss agents.

Sad Puppies and SJWs: Symbolic Revolution and Challenges to Field Orthodoxy in the Struggle for Control of Science Fiction’s Hugo Awards

ABSTRACT In 2013 a group of science fiction authors launched a campaign to reform the Hugos, one of science fiction’s most prestigious awards. Dubbing themselves “Sad Puppies,” these activists sought to correct what they viewed as a political imbalance. Science fiction, they argued, had moved away from entertaining story telling in favor of heavy-handed message fiction meant to spread pernicious liberal values. This article draws on an analysis of internet posts by actors involved in the movement to examine the Puppies’ efforts to reform the science fiction field, reclaiming it as a space for conservative authors and fans. I argue that the leaders of the movement were able to build small fan communities into a larger social movement by framing the movement in such a way as to link their efforts to exogenous political and cultural debates occurring in proximate social fields. This analysis demonstrates the processes by which tournament rituals in popular culture can come to serve as contests of values, arenas within which larger political and cultural conflicts are waged.

Fear appeals in Chinese public signs of COVID-19 prevention in local communities

Abstract Guided by the Extended Parallel Process Model (EPPM), this paper explicates the messaging strategies in Chinese public signs of COVID-19 prevention in local communities. 162 signs were collected from Internet posts. Our results show that the EPPM is a viable fear appeal framework to explain the communication of public health risks. Most signs communicated the threat of the virus to the public, whereas fewer signs emphasized the efficacy to effectively control the threat. In addition to communicating individual threat and efficacy, quite a few signs also highlighted collective threat and efficacy. Moreover, the language used in these signs is tailored to local cultural and social conventions. These findings not only contribute to the growing body of research on the interpersonal function of public signage from a Chinese perspective, but also demonstrate the utility of combining pragmatic research with messaging strategies in health communication research.

Interaction, Heteroglossia and Discourse in the #maternidadereal [realmaternity

ABSTRACT This article is the result of a doctoral study about the maternal ethos in social networks and it aims to reflect on the expression #maternidadereal [real maternity], used in internet posts. In the light of Mikhail Bakhtin's concept of heteroglossia, this observation will start from the idea of interaction, a fundamental and constituent phenomenon in social networks. the meaning of “real” will be considered from Jacques Lacan’s theory, to delineate the differences between the hegemonic discourse of traditional motherhood, of romanticization, and the contemporary and counter-hegemonic discourse, through which motherhood can be vented in its pains. Finally, in order to verify how these discourses intersect in the materiality of language, 5 posts with the tag #maternidadereal will be analyzed, determining, through their brands, which axiological axis they are affiliated with. As a result, it was noticed that the idea of “real” fluctuates according to the subject-position of the mother who appropriates it, evidencing the heteroglossia that underlies these statements.

The impact of internet articles on investor trading decisions by investor types: Evidence from Korean stock market

This study investigates the impact of internet articles, such as blogs or internet community posts, on investors’ trading decisions in response to uncertain news (i.e., rumors). Leveraging the unique disclosure requirement in Korea, which verifies rumors via firm disclosures, we are able to identify trading activities associated with rumors. We find that internet article coverage affects individual investors’ decisions, but not those of institutional investors. Moreover, the post-disclosure returns deteriorate only for individual investors with internet article coverage. Overall, these results suggest that internet articles adversely affect individual investors’ trading decisions on rumors, resulting in a wealth transfer from individual investors to institutional investors.

An online world of bias. The mediating role of cognitive biases on extremist attitudes

Abstract Extremists often aim to paint a biased picture of the world. Radical narratives, for instance, in forms of internet memes or posts, could thus potentially trigger cognitive biases in their users. These cognitive biases, in turn, might shape the users’ formation of extremist attitudes. To test this association, an online experiment (N=392) was conducted with three types of right-wing radical narratives (elite-critique, ingroup-outgroup, violence) in contrast to two control conditions (nonpolitical and neutral political control condition). We then measured the impact of these narratives on the activation of three cognitive biases of relevance in the formation of extremist attitudes: the ingroup-outgroup bias, the negativity bias, and the just-world hypothesis. The results indicate that violence narratives seem to be particularly harmful as they heighten participants’ negativity bias and increase just-world views. Just-world views in turn show a positive relationship to extremist attitudes, which highlights the need of regulating violence invocations on social media.

EFL Learning and Self-identity Development: A Case Study

This 2-year qualitative study examined the EFL learning and self-identity development of three college students from a university in Beijing, China. Data collected included interview transcripts, field notes of observations, participants’ Internet postings, participants’ academic works, and so on. Giddens’ theory of reflexivity of self-identity was adopted as a theoretical framework. Norton’s theory of investment was also used in the case analysis. Results revealed that EFL learners constructed multiple identities in their multiple investments, which represented their ambivalent desires in learning and using English; EFL learners could monitor their learning process reflexively and exercise agency in drawing on different resources selectively to construct their ideal identities; unintended consequences were produced by learners’ acts, which in turn served as conditions of their further acts.

Political Security Threat Prediction Framework Using Hybrid Lexicon-Based Approach and Machine Learning Technique

The internet offers a powerful medium for expressing opinions, emotions and ideas, using online platforms supported by smartphone usage and high internet penetration. Most internet posts are textual based and can include people’s emotional feelings for a particular moment or sentiment. Monitoring online sentiments or opinions is important for detecting any excessive emotions triggered by citizens which can lead to unintended consequences and threats to national security. Riots and civil war, for instance, must be addressed due to the risk of jeopardizing social stability and political security, which are crucial elements of national security. Mining opinions according to the national security domain is a relevant research topic that must be enhanced. Mechanisms and techniques that can mine opinions in the aspect of political security require significant improvements to obtain optimum results. Researchers have noted that there is a strong relationship between emotion, sentiment and political security threats. This study proposes a new theoretical framework for predicting political security threats using a hybrid technique: the combination of lexicon-based approach and machine learning in cyberspace. In the proposed framework, Decision Tree, Naive Bayes, and Support Vector Machine have been deployed as threat classifiers. To validate our proposed framework, an experimental analysis is accomplished. The performance of each technique used in the experiments is reported. In this study, our proposed framework reveals that the hybrid Lexicon-based approach with the Decision Tree classifier recorded the highest performance score for predicting political security threats. These findings offer valuable insight to ongoing research on opinion mining in predicting threats based on the political security domain.

A Case about Infringing a Physician's Right of Reputation: Legal Liability for Internet Postings Made by a Medical Clinic

A Case about Infringing a Physician's Right of Reputation: Legal Liability for Internet Postings Made by a Medical Clinic

Способы выражения общественного настроения в текстах интернет-сообщества политической направленности социальной сети «ВКонтакте»

The article discusses the most typical ways of expressing public mood that are employed by the participants in discussions in a politically-oriented Internet community. Public mood is understood as a form of mass political consciousness’ existence. The study was conducted on the material of texts posted in the Politika (Politics) community of the VKontakte social network from September 21, 2022 to January 6, 2023. On the basis of the Internet posts and comments to them, the research revealed the preferences of Internet users in choosing the pragmatic type of utterance. The pragmatic type is determined by the predominance of a certain component of public moods (emotional, rational, or rational-emotional). The correspondence between the prevailing type of the Internet users’ reaction and the pragmatic type of utterance was established taking into account the speech act theory developed by J. Austin and J. Searle and the typology of speech acts proposed by N. I. Formanovskaya. The paper points out the predominance of the emotive dominant in the structure of the Russian-speaking Internet users’ public attitudes. It is realized in the employment of mostly rogative, directive, and representative speech acts in the texts under analysis. Other types of speech acts (expressives, contactives) mainly implement the phatic function of communication in the Internet community. The paper concludes that the use of the identified types of speech acts contributes to the expression of subjective experiences of the community members. It also allows the authors of the texts to propose their own scenarios for the development of the political situation under discussion and to influence the formation of attitudes of other Vkontakte users.

“It’s Getting Difficult to Be a Straight White Man”: Bundled Masculinity Grievances on Reddit

This article examines a case of internet posts discussing social issues affecting men and masculinity. Analysis of 500 posts containing masculine coded language on the subreddit r/unpopularopinion suggests that masculinity, especially when intersected with straightness and whiteness, is discursively constructed in an imagined social hierarchy where the plight of straight white men is invisible. By framing opinions as “unpopular,” these posts suggest that while the poster’s view may be objectively true, it is disvalued in mainstream discourses. Three key findings emerged from this analysis: First, regardless of the particular social issue discussed, efforts to reduce social inequality were negatively evaluated on average. Second, negative posts were more popular on the site; thus, amplifying the visibility of grievances. Third, masculine coded language is structured on Reddit, such that certain issues are bundled together to generate salient, interlocking themes indicating a robust meaning system. Overall, these findings suggest that criticisms of social equality are embedded within a discourse of threatened masculinity, straightness, and whiteness. This research extends past work on internet discursive practices related to masculinity and gender by showing the pervasiveness and intersectional nature of masculinity threat in digital forms.

Internet postings and investor herd behavior: evidence from China’s open-end fund market

AbstractThe popularity of social media facilitates the dissemination of private information, which has significant implications for investors’ behavior and market efficiency. This paper examines the dynamic dependence between internet postings and herd behavior in China’s open-end fund market by applying the DCC-GARCH and TVP-SV-VAR models. The results show that the relationship between internet postings and herd behavior is time-varying and asymmetric. Specifically, internet postings have a negative effect on herd behavior, and the effect is more pronounced in the short term. Additionally, herd behavior will increase postings and further weaken the herding effect through the internet postings channel. Our results also show that the increase and decrease of postings have asymmetric effects on herd behavior, and the increase of postings has a greater effect on herd behavior.

Queer and Disabled: Voices from People Who Have Been Hidden

ABSTRACT There is a reluctance to discuss sex and gender within the context of special education. Often, the assumption is that individuals with disabilities are asexual or too immature to have thoughts about sex and sexuality. Misconceptions about individuals with disabilities concerning sex and sexuality exclude this population from resources or avenues to explore this very important aspect of being human. Individuals with disabilities who also identify as queer face further isolation from support and a sense of community. The group of people who identify as disabled and queer struggle to find support from either of the intersectional Identities. The queer and disabled population is often excluded from the queer community because of a disability while at the same time being ostracized from the support of the disability community because they are queer. This article investigates data collected from Internet postings, involving individuals who self-identify as queer and disabled to examine issues and concerns faced by this unseen population. In these data, individuals expose their feelings about sex, sexuality, gender identity, access, and disability as they express their very personal accounts of living in two worlds. These analyzed narratives uncover the voices of an intersectional population who the ableist and heteronormative culture has ignored and pushed aside. The goal of an exploration into the perspectives and experiences of the project contributors is to provide insight that eventually leads to understanding and enables more effective support systems to be provided for individuals who simultaneously identify as queer and disabled.

Deep fake news detection system based on concatenated and recurrent modalities

With the rise in popularity of social media platforms and online forums, they have become a global source of news. Fake News (FNs) spreading through numerous institutions and sectors jeopardizes their reputations, causing users to abandon these platforms. Therefore, there is a huge pool of research in the area of Artificial Intelligence (AI) techniques that are used to detect FNs. Previously, great focus was given to online review classification and free internet posts based on social networks. This research proposes a Deep Learning-based FNs Detection method. This paper proposes a Deep Learning (DL)-based method for detecting FNs. The proposed system consists of three phases; text encoding, feature extraction, and classification. The text encoding process is carried out on the input news words using GLOVE for word representation. The encoded words are then embedded into a specific word length in order to be enrolled in the proposed DL models. The proposed DL models comprise both automatic feature extraction and classification tasks. Furthermore, this study proposes four different DL models, including Convolutional Neural Networks (CNNs) and Concatenated CNNs (C-CNNs), long short-term memory (LSTM), and Gated Recurrent Units, to find an optimal model prior to the section of FNs that outperforms previous works.The proposed DL models are carried out on FNs and FNC datasets which are provided by kaggle, and the suggested C-CNNs algorithm obtained an accuracy of 99.6% and trained faster than others. Multiple evaluation metrics such as precision, recall, F1, and accuracy have been utilized to evaluate the outcome of the proposed models. The experimental results demonstrated overall improvements in the subject of FND when compared with the current models and validated the potential of the proposed methodology for the detection of FNs on Social Media (SM). This study will help researchers to broaden the knowledge of applications of CNNs based on DL methods for FND.

A Preliminary Study of Clinical Psychology Doctoral Students' Training in Food Insecurity Assessment and Resource Provision.

Food insecurity (FI) is common among people with mental illness and is associated with poor health outcomes, suggesting that equipping future psychologists with skills to address FI would be beneficial. We assessed basic FI knowledge and training among clinical psychology doctoral students in the United States and Canada. Graduate students were recruited through psychology listservs and internet postings to complete an online survey about their training experience in FI assessment and resource provision. Students (n=155) reported familiarity with the term FI but not with resources to address it. Less than 10% of students had received any training in FI assessment or resource provision as part of their graduate training, but the majority (over 80%) wished they had received this training. Clinical psychology doctoral students generally do not receive training in FI assessment and resource provision but want this training. Graduate programs should consider adding curricula to address this gap.