scholarly journals Crisis and Disaster Situations on Social Media Streams: An Ontology-Based Knowledge Harvesting Approach

10.28945/4420 ◽  
2019 ◽  
Vol 14 ◽  
pp. 343-366
Author(s):  
SenthilKumar Narayanasamy ◽  
Dinakaran Muruganantham ◽  
Atilla Elçi
Keyword(s):  
Decision Making ◽  
Social Media ◽  
Information Retrieval ◽  
Language Processing ◽  
News Media ◽  
News Coverage ◽  
Collaborative Problem Solving ◽  
Future Research ◽  
Semantic Web Technologies ◽  
News Content

Aim/Purpose: Vis-à-vis management of crisis and disaster situations, this paper focuses on important use cases of social media functions, such as information collection & dissemination, disaster event identification & monitoring, collaborative problem-solving mechanism, and decision-making process. With the prolific utilization of disaster-based ontological framework, a strong disambiguation system is realized, which further enhances the searching capabilities of the user request and provides a solution of unambiguous in nature. Background: Even though social media is information-rich, it has created a challenge for deriving a decision in critical crisis-related cases. In order to make the whole process effective and avail quality decision making, sufficiently clear semantics of such information is necessary, which can be supplemented through employing semantic web technologies. Methodology: This paper evolves a disaster ontology-based system availing a framework model for monitoring uses of social media during risk and crisis-related events. The proposed system monitors a discussion thread discovering whether it has reached its peak or decline after its root in the social forum like Twitter. The content in social media can be accessed through two typical ways: Search Application Program Interfaces (APIs) and Streaming APIs. These two kinds of API processes can be used interchangeably. News content may be filtered by time, geographical region, keyword occurrence and availability ratio. With the support of disaster ontology, domain knowledge extraction and comparison against all possible concepts are availed. Besides, the proposed method makes use of SPARQL to disambiguate the query and yield the results which produce high precision. Contribution: The model provides for the collection of crisis-related temporal data and decision making through semantic mapping of entities over concepts in a disaster ontology we developed, thereby disambiguating potential named entities. Results of empirical testing and analysis indicate that the proposed model outperforms similar other models. Findings: Crucial findings of this research lie in three aspects: (1) Twitter streams and conventional news media tend to offer almost similar types of news coverage for a specified event, but the rate of distribution among topics/categories differs. (2) On specific events such as disaster, crisis or any emergency situations, the volume of information that has been accumulated between the two news media stands divergent and filtering the most potential information poses a challenging task. (3) Relational mapping/co-occurrence of terms has been well designed for conventional news media, but due to shortness and sparseness of tweets, there remains a bottleneck for researchers. Recommendations for Practitioners: Though metadata avails collaborative details of news content and it has been conventionally used in many areas like information retrieval, natural language processing, and pattern recognition, there is still a lack of fulfillment in semantic aspects of data. Hence, the pervasive use of ontology is highly suggested that build semantic-oriented metadata for concept-based modeling, information flow searching and knowledge exchange. Recommendation for Researchers: The strong recommendation for researchers is that instead of heavily relying on conventional Information Retrieval (IR) systems, one can focus more on ontology for improving the accuracy rate and thereby reducing ambiguous terms persisting in the result sets. In order to harness the potential information to derive the hidden facts, this research recommends clustering the information from diverse sources rather than pruning a single news source. It is advisable to use a domain ontology to segregate the entities which pose ambiguity over other candidate sets thus strengthening the outcome. Impact on Society: The objective of this research is to provide informative summarization of happenings such as crisis, disaster, emergency and havoc-based situations in the real world. A system is proposed which provides the summarized views of such happenings and corroborates the news by interrelating with one another. Its major task is to monitor the events which are very booming and deemed important from a crowd’s perspective. Future Research: In the future, one shall strive to help to summarize and to visualize the potential information which is ranked high by the model.

scholarly journals News values on social media: Exploring what drives peaks in user activity about organizations on Twitter

Journalism ◽  
2018 ◽  
Vol 21 (5) ◽  
pp. 633-651 ◽  
Author(s):  
Theo Araujo ◽  
Toni GLA van der Meer
Keyword(s):  
Social Media ◽  
News Media ◽  
Social Impact ◽  
News Coverage ◽  
Value Theory ◽  
News Values ◽  
News Value ◽  
Twitter Users ◽  
Nuanced Understanding ◽  
Twitter Activity

Since news circulation increasingly takes place online, the public has gained the capacity to influence the salience of topics on the agenda, especially when it comes to social media. Considering increased scrutiny about organizations, this study aims to understand what causes heightened activity to organization-related topics among Twitter users. We explore the extent to which news value theory, news coverage, and influential actors can explain peaks in Twitter activity about organizations. Based on a dataset of 1.8 million tweets about 18 organizations, the findings show that the news values social impact, geographical closeness, facticity, as well as certain influential actors, can explain the intensity of online activities. Moreover, the results advocate for a more nuanced understanding of the relation between news media and social media users, as indications of reversed agenda-setting patterns were observed.

scholarly journals Analysing inter-media agenda setting influence between Social media and electronic media; a perspective from Pakistan

2020 ◽  
Vol Volume 4 (Issue 2) ◽  
pp. 478-496
Author(s):  
Farrukh Shahzad ◽  
Prof. Dr. Syed Abdul Siraj
Keyword(s):  
Social Media ◽  
News Media ◽  
Agenda Setting ◽  
News Coverage ◽  
Electronic Media ◽  
Analysis Method ◽  
Traditional Media ◽  
Media Agenda ◽  
Content Analysis Method ◽  
Lagged Correlation

Inter-media agenda setting is a commonly used phenomenon to investigate the transfer of contents between news media. The recent digitization era challenges the traditional presuppositions. This study investigates the inter-media agenda setting influence between social media and traditional media. To address this question, the present study investigates first level agenda setting between Twitter and ARY news during Farishta murder case 2019. Content analysis method was used to assess agendas present within Twitter and ARY news. By employing cross-lagged correlation, the study investigates the inter-media agenda setting influence between Twitter agendas and of ARY news agendas. Aggregate findings of cross-lagged correlation reveal a clear agenda setting influence of Twitter on ARY news coverage agenda about Farishta murder case. The results of the study suggest that Twitter has the capability to influence broadcast agendas of television in Pakistan

The Hostile Media Effect

2014 ◽  
Author(s):  
Lauren Feldman
Keyword(s):  
Public Opinion ◽  
News Media ◽  
Observational Studies ◽  
News Coverage ◽  
Consumption Patterns ◽  
Future Research ◽  
Psychological Processes ◽  
Media Effect ◽  
Hostile Media Effect ◽  
Hostile Media

The “hostile media effect” occurs when opposing partisans perceive identical news coverage of a controversial issue as biased against their own side. This is a robust phenomenon, which has been empirically demonstrated in numerous experimental and observational studies across a variety of issue contexts and has been shown to have important consequences for democratic society. This chapter reviews the literature on the hostile media effect with an eye toward the theoretical explanations for it, its relationship to other psychological processes, and its broader implications for perceived public opinion, news consumption patterns, attitudes toward democratic institutions, and political discourse and participation. Particular attention is paid to how the hostile media phenomenon can help explain the public’s eroding trust in the news media and the recent polarization among news audiences. The chapter concludes with several suggestions for future research.

Gender Stereotyping in Political Decision Making

2019 ◽  
Author(s):  
Nichole M. Bauer
Keyword(s):  
Decision Making ◽  
Gender Stereotypes ◽  
News Media ◽  
News Coverage ◽  
Political Leadership ◽  
The United States ◽  
Political Decision ◽  
Female Candidates ◽  
Campaign Messages ◽  
Political Decision Making

Women are under-represented at every level of elected office in the United States. As of 2018, women held just under 20% of seats in Congress, 25% of state legislative seats across the country, only six women serve as governor, and, of course, a woman has yet to win the presidency. The political under-representation of women is not unique to the American context. Indeed, women’s under-representation is a feature of other Western Democracies. Even under the leadership of female prime ministers, women hold only 32% of seats in the United Kingdom parliament and 31% of seats in the German parliament. Conventional wisdom suggests that feminine stereotypes may disadvantage female candidates. Feminine stereotypes characterize women as sensitive, emotional, and weak, and these are qualities voters do not traditionally associate with political leadership. Rather, voters associate political leadership with masculine traits such as being tough, aggressive, or assertive. The extent to which voters use these stereotypes in political decision making in the American context is not entirely clear. There are three ways that feminine and masculine stereotypes can affect political decision making: candidate strategies, campaign news coverage, and vote choice decision. The alignment between masculine stereotypes and political leadership frequently pressures female candidates to emphasize masculine qualities over feminine qualities in campaign messages. Motivating these masculine messages is the perception that voters see female candidates as lacking the masculine qualities voters desire in political leaders. Male candidates, because of the alignment between masculinity and leadership roles, do not face this pressure. Female candidates will, however, highlight feminine stereotypes when these strategies will afford them a distinct electoral advantage. The use of masculinity in candidate strategy leads the news media, in turn, to use masculine stereotypes rather than feminine stereotypes in their coverage of both female and male candidates. The ways that candidates and the news media engage with gender stereotypes affects how voters use these concepts to form impressions of female and male candidates. Voters will use feminine stereotypes as heuristics to form impressions of the ideological and issue priorities of female candidates. Feminine stereotypes can hurt the electoral prospects of female candidates, but the negative effect of feminine stereotypes only occurs under a limited set of conditions. Voters will use feminine stereotypes to rate female candidates negatively when female candidates explicitly emphasize feminine qualities, such as being warm or compassionate, in campaign messages. But, voters respond positively to female candidates who emphasize positive masculine qualities. In sum, whether gender stereotypes affect voter decision-making depends on the extent to which voters see messages, either from campaigns or the news media, that reflect femininity or masculinity.

scholarly journals A topic analysis of traditional and social media news coverage of the early COVID-19 pandemic and implications for public health communication

2020 ◽  
Author(s):  
Wallace Chipidza ◽  
Elmira Akbaripourdibazar ◽  
Tendai Gwanzura ◽  
Nicole M. Gatto
Keyword(s):  
Public Health ◽  
Social Media ◽  
Health Communication ◽  
News Media ◽  
Latent Dirichlet Allocation ◽  
News Coverage ◽  
Cable News ◽  
Public Health Communication ◽  
Health Related ◽  
Public Health Officials

AbstractKnowledge gaps may initially exist among scientists, medical and public health professionals during pandemics, which are fertile grounds for misinformation in news media. We characterized and compared COVID-19 coverage in newspapers, television, and social media, and discussed implications for public health communication strategies that are relevant to an initial pandemic response. We conducted a Latent Dirichlet Allocation (LDA), an unsupervised topic modelling technique, analysis of 3,271 newspaper articles, 40 cable news shows transcripts, 96,000 Twitter posts, and 1,000 Reddit posts during March 4 - 12, 2020, a period chronologically early in the timeframe of the COVID-19 pandemic. Coverage of COVID-19 clustered on topics such as epidemic, politics, and the economy, and these varied across media sources. Topics dominating news were not predominantly health-related, suggesting a limited presence of public health in news coverage in traditional and social media. Examples of misinformation were identified particularly in social media. Public health entities should utilize communication specialists to create engaging informational content to be shared on social media sites. Public health officials should be attuned to their target audience to anticipate and prevent spread of common myths likely to exist within a population. This will help control misinformation in early stages of pandemics.

scholarly journals Discovering Cohorts of Pregnant Women From Social Media for Safety Surveillance and Analysis (Preprint)

2017 ◽  
Author(s):  
Abeed Sarker ◽  
Pramod Chandrashekar ◽  
Arjun Magge ◽  
Haitao Cai ◽  
Ari Klein ◽  
...  
Keyword(s):  
Social Media ◽  
Pregnant Women ◽  
Language Processing ◽  
Classification System ◽  
Future Research ◽  
Sources Of Information ◽  
Blind Test ◽  
Other Information ◽  
Positive Class ◽  
The Many

BACKGROUND Pregnancy exposure registries are the primary sources of information about the safety of maternal usage of medications during pregnancy. Such registries enroll pregnant women in a voluntary fashion early on in pregnancy and follow them until the end of pregnancy or longer to systematically collect information regarding specific pregnancy outcomes. Although the model of pregnancy registries has distinct advantages over other study designs, they are faced with numerous challenges and limitations such as low enrollment rate, high cost, and selection bias. OBJECTIVE The primary objectives of this study were to systematically assess whether social media (Twitter) can be used to discover cohorts of pregnant women and to develop and deploy a natural language processing and machine learning pipeline for the automatic collection of cohort information. In addition, we also attempted to ascertain, in a preliminary fashion, what types of longitudinal information may potentially be mined from the collected cohort information. METHODS Our discovery of pregnant women relies on detecting pregnancy-indicating tweets (PITs), which are statements posted by pregnant women regarding their pregnancies. We used a set of 14 patterns to first detect potential PITs. We manually annotated a sample of 14,156 of the retrieved user posts to distinguish real PITs from false positives and trained a supervised classification system to detect real PITs. We optimized the classification system via cross validation, with features and settings targeted toward optimizing precision for the positive class. For users identified to be posting real PITs via automatic classification, our pipeline collected all their available past and future posts from which other information (eg, medication usage and fetal outcomes) may be mined. RESULTS Our rule-based PIT detection approach retrieved over 200,000 posts over a period of 18 months. Manual annotation agreement for three annotators was very high at kappa (κ)=.79. On a blind test set, the implemented classifier obtained an overall F1 score of 0.84 (0.88 for the pregnancy class and 0.68 for the nonpregnancy class). Precision for the pregnancy class was 0.93, and recall was 0.84. Feature analysis showed that the combination of dense and sparse vectors for classification achieved optimal performance. Employing the trained classifier resulted in the identification of 71,954 users from the collected posts. Over 250 million posts were retrieved for these users, which provided a multitude of longitudinal information about them. CONCLUSIONS Social media sources such as Twitter can be used to identify large cohorts of pregnant women and to gather longitudinal information via automated processing of their postings. Considering the many drawbacks and limitations of pregnancy registries, social media mining may provide beneficial complementary information. Although the cohort sizes identified over social media are large, future research will have to assess the completeness of the information available through them.

scholarly journals Artificial Intelligence in News Media: Current Perceptions and Future Outlook

2021 ◽  
Author(s):  
Mathias-Felipe de-Lima-Santos ◽  
Wilson Ceron
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Computer Vision ◽  
Science Fiction ◽  
Language Processing ◽  
News Media ◽  
Future Research ◽  
Production And Distribution ◽  
News Industry ◽  
Journalistic Field

In recent years, news media has been greatly disrupted by the potential of technologically driven approaches in the creation, production, and distribution of news products and services. Artificial intelligence (AI) has emerged from the realm of science fiction and has become a very real tool that can aid society in addressing many issues, including the challenges faced by the news industry. The ubiquity of computing has become apparent and has demonstrated the different approaches that can be achieved using AI. We analyzed the news industry’s AI adoption based on the seven subfields of AI: (i) machine learning; (ii) computer vision (CV); (iii) speech recognition; (iv) natural language processing (NLP); (v) planning, scheduling, and optimization; (vi) expert systems; and (vii) robotics. Our findings suggest that three subfields are being developed more in the news media: machine learning, computer vision, as well as planning, scheduling, and optimization. Other areas have not been fully deployed in the journalistic field. Most AI news projects rely on funds from tech companies such as Google. This limits AI’s potential to a small number of players in the news industry. We make conclusions by providing examples of how these subfields are being developed in journalism and present an agenda for future research.

scholarly journals From the Deeming Rule to JUUL—US News Coverage of Electronic Cigarettes, 2015–2018

2020 ◽  
Vol 22 (10) ◽  
pp. 1816-1822 ◽  
Author(s):  
Olivia A Wackowski ◽  
Jennah M Sontag ◽  
Binu Singh ◽  
Jessica King ◽  
M Jane Lewis ◽  
...  
Keyword(s):  
News Media ◽  
Electronic Cigarettes ◽  
News Coverage ◽  
High Volume ◽  
The United States ◽  
Online News ◽  
Future Research ◽  
The Public ◽  
News Sources ◽  
Over Time

Abstract Introduction News media may influence public perceptions and attitudes about electronic cigarettes (e-cigarettes), which may influence product use and attitudes about their regulation. The purpose of this study is to describe trends in US news coverage of e-cigarettes during a period of evolving regulation, science, and trends in the use of e-cigarettes. Methods We conducted a content analysis of e-cigarette topics and themes covered in US news articles from 2015 to 2018. Online news databases (Access World News, Factiva) were used to obtain US news articles from the top 34 circulating newspapers, four national wire services, and five leading online news sources. Results The number of articles increased by 75.4% between 2015 and 2018 (n = 1609). Most articles focused on policy/regulation (43.5%) as a main topic, followed by health effects (22.3%) and prevalence/trends (17.9%). Discussion about flavor bans quadrupled (6.1% to 24.6%) and discussion of youth e-cigarette use was most prevalent (58.4%) in 2018, coinciding with an increase in coverage about JUUL. JUUL was mentioned in 50.8% of 2018 articles. Across years, articles more frequently mentioned e-cigarette risks (70%) than potential benefits (37.3%). Conclusions E-cigarettes continue to be a newsworthy topic, with coverage both reflecting numerous changes and events over time, and providing repeated opportunities for informing the public and policymakers about these novel products. Future research should continue to track how discourse changes over time and assess its potential influence on e-cigarette perceptions and policy changes. Implications E-cigarette news coverage in the United States increased between 2015 and 2018 and predominantly focused on policy and regulation. Notable spikes in volume were associated with some but not all major e-cigarette events, including the FDA’s deeming rule, Surgeon General’s report, and release of the National Youth Tobacco Survey data in 2018. Coverage of the 2018 National Academy of Medicine, Engineering, and Sciences report on the Public Health Consequences of E-cigarettes received minimal news coverage. The high volume in 2018 was driven in large part by coverage of the e-cigarette brand JUUL; over half of news articles in 2018 referenced JUUL specifically.

Spreading the News

2019 ◽  
pp. 695-710
Author(s):  
Robert J. Baron
Keyword(s):  
Social Media ◽  
News Media ◽  
Rhetorical Analysis ◽  
News Content ◽  
Communication Environments

This chapter is concerned with the process of “spreading the news” through social media. It suggests a method of rhetorical analysis that focuses less attention on the content of news productions and more attention on analyzing how audience members might make use of the news. This transition of focus from content to audience use should lead news producers to see the value of content that engages with audiences and enables audience members to engage in the sharing and spreading of news content. The purpose of this chapter is twofold: 1) to help journalists design content that engages their audiences in the process of spreading the news in ways that can go far beyond the reach of traditional news formats and 2) to provide journalism scholars with a means of understanding the ways in which audiences and the news media interact in social media-rich communication environments.

The Role of Social Commerce Components on the Consumer Decision-Making Process

2021 ◽  
pp. 227-239
Author(s):  
Saleh Mohammed Kutabish ◽  
Ana Maria Soares
Keyword(s):  
Decision Making ◽  
Social Media ◽  
Future Research ◽  
Decision Making Process ◽  
Consumer Decision Making ◽  
Social Commerce ◽  
Consumer Decision ◽  
Rapid Changes ◽  
Learning From Others

Rapid changes in commerce, technology, and consumer behaviour are leading businesses to shift their online activities. The popularity of social media pushed online merchants to integrate these platforms into their online presence, leading to the rise of social commerce. Consumers' interaction and participation online create a massive amount of information. The use of social commerce components facilitates the interaction of consumers by sharing their experiences and learning from others' experiences. In this chapter, the authors look at how this process has impacts throughout the consumer decision-making process when making a purchase and suggests directions for future research.

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