Message Topics Research Articles

Геоботаника на XIV Делегатском съезде Русского ботанического общества и конференции «Ботаника в современном мире» (г. Махачкала, 18–23 июня 2018 г.)

The XIV Congress of the Russian Botanical Society (RBO) and the conference «Botany in the Modern World» were held at 18–23 of June 2018 in Makhachkala (the Republic of Dagestan) on the bases of Dagestan Research Center of Russian Academy of Sciences and Dagestan State University. Materials of the conference were published in 3 volumes by the beginning of meeting. Geobotanical publications are included in the second volume (Botany in the modern world …, 2018). The analysis of the materials shows that participants are represented 26 cities of Russia, 4 of Belarus and Kazakhstan and 1 of Australia. Research data are collected in 36 regions of Russia, 2 of Kazakhstan, as well as in Belarus, Azerbaijan and Egypt, in addition, 4 generalizing works are performed on large volumes of geobotanical relevés from all over Europe or Russia. The data on forests are analyzed in 18 reports, that ones on meadows formed the basis for 12 presented works, 9 communications are based on data of wetlands, 7 works are devoted to steppe vegetation, 5 — to tundra, 5 — to shrub communities, 4 — to vegetation of saline habitats, 4 — to pioneer plant groupings, 3 — to synanthropic vegetation, 2 communications are based on aquatic vegetation. Data of field experiment, shibliak, xeric open forests, deserts, friganoid and petrophytic vegetation are each the base of single report. The message topics were very diverse. Regional characteristic of various groups of plant communities and/or their classification is reflected in 38 studies; 14 researches are devoted to environment-vegetation or environment-plant interactions; results of 13 reports are based on vegetation mapping or obtained for the purpose of studying the geography of plant communities and geobotanical mapping; 11 communications are devoted to dynamics of vegetation and structure of phytocenoses; the main emphasis of 10 researches is a study of species composition of plant communities; data of 9 works are collected on existing and proposed specially protected natural areas, these studies are related to the nature protection; in 8 reports special attention is paid to the development of geobotanical research methods and phytosociological terminology; in 5 works the structure of populations of plant species is analyzed. The Congress that was held at a good scientific level reflected the current state of geobotanical science in Russia.

The Role of Construal Level in Message Effects Research: A Review and Future Directions

Abstract Construal level theory is a recent psychological theory that examines the relationship between psychological distance and mental construal. However, the theory’s implications for message effects research requires more attention. Construal level theory can guide new hypotheses that test the persuasiveness of certain combinations of message features and tailoring messages to different individual orientations. This is possible as the theory may reveal underlying similarities across disparate message topics, design elements, and message processing styles. Core principles of construal level theory are explicated, and message effects research areas that may benefit from its propositions are reviewed and elaborated upon. Finally, caveats of applying the theory to message effects research and future directions are discussed.

Understanding Secure Messaging in the Inpatient Environment: A New Avenue for Communication and Patient Engagement.

Patient portals, and the secure messaging feature in particular, have been studied in the outpatient setting, but research in the inpatient setting is relatively less mature. To understand the topics discussed in secure messaging in the inpatient environment, we analyzed and categorized messages sent within an inpatient portal. This observational study examined the content of all secure messages sent from December 2013 to June 2017 within an inpatient portal at a large Midwestern academic medical center (AMC). We analyzed a total of 2,598 messages, categorizing them by sender (patient, family, or care team member), type, and topic, and conducted a descriptive analysis of categories and an examination of code co-occurrence. Patients were the most frequent message senders (63%); family members sent the fewest messages (10%). We identified five types of messages: Alert/Request; Thanks; Response; Question; and Other (typo/test message). Patient messages included Alerts/Requests (38%), Questions (31%), Statements of Thanks (24%), Response (1.2%), and Other (5%). We also identified 14 nonmutually exclusive message topics: Medication; Procedure/Treatment Plan; Schedule; Pain; Results; Diet; Discharge; Non-Medication Questions; Provider Requests; Symptoms; Custodial; Technical Issues; Potential Error; and Contact Information. Patient message topics most commonly discussed Symptoms (18%), Procedure/Treatment Plan (14%), or Pain (12%). Our analysis of secure message content suggests certain message types and topics such as Alerts/Requests and Questions about symptoms and treatment plans are particularly important to patients. These findings demonstrate that both patients and family members utilize the secure messaging function to engage in the care process by posing questions, making requests, and alerting staff to problems. As this technology is implemented in additional facilities, future work should examine how use of secure messaging may be influenced by factors including patients' demographics, reasons for hospitalization, and length of stay.

Examining the impact of message frames on information seeking and processing

PurposeThe purpose of this paper is to apply the situational theory of publics and framing theory in the context of environmental corporate social responsibility (CSR) communication research to determine how message frames impact information seeking and processing.Design/methodology/approachUsing a 3 (message frame: diagnostic, prognostic, or motivational) × 2 (environmental issue: general vs specific) plus control between subjects experimental design, the study examines the attitudes, cognitions, and behavioral intentions different publics may form about two different environmental responsibility issues presented in this study. Furthermore, the study aims to examine how different types of message frames (diagnostic, prognostic, or motivational) and topics may impact how a company can move a public toward information seeking behaviors.FindingsBased on theoretical considerations, structural equation modeling was used to examine significant paths between variables, thus creating a proposed new theoretical model that can be applied to CSR, public relations, and strategic communication literature.Originality/valueThe study offers a proposed new integrated theoretical model that can be applied to strategic communication literature and used to assist companies with enhancing their CSR communication and strategic communication planning efforts to determine how to move a public toward information seeking behaviors.

Meta-Analysis of the Persuasive Effects of Metaphorical vs. Literal Messages

This study uses meta-analysis to compare the persuasive effects of metaphorical and literal messages and assess various theoretical explanations for the relatively greater persuasiveness of metaphor. The meta-analysis includes studies from 2001 to 2015 that use various message formats and topics. Results indicate an overall effect size of r = 0.09, p < .001, 95% CI [0.06, 0.12], which supports the conclusion that metaphorical messages are more persuasive than literal messages. Moderator analyses indicate differences in effect sizes based on metaphor target familiarity, message topic, and message format. A meta-regression using the theoretical moderators indicates that message format was the strongest predictor of variation in effect size. This study provides implications related to the theoretical mechanism behind the greater persuasive effects of metaphor.

The MomConnect helpdesk: how an interactive mobile messaging programme is used by mothers in South Africa.

South Africa’s MomConnect mobile messaging programme, which aims to promote safe motherhood and improve pregnancy outcomes for South African women, includes a helpdesk feature which allows women registered on the system to ask maternal and child health (MCH)-related questions and to provide feedback on health services received at public health clinics. Messages sent to the helpdesk are answered by staff located at the National Department of Health. We examined event data from the MomConnect helpdesk database to identify any patterns in messages received, such as correlation of frequency or types of messages with location. We also explored what these data could tell us about the helpdesk’s effectiveness in improving health service delivery at public health clinics. We found that approximately 8% of registered MomConnect users used the helpdesk, and that usage was generally proportional to the use of antenatal care (ANC) services in provinces (as indicated by number of ANC first visits and number of MomConnect registrations), except in two provinces. Language, category and key topics of helpdesk messages were correlated with provinces. Most users accessed the helpdesk to seek maternal information, and where feedback about health services was provided, there were significantly more compliments than complaints. The MomConnect helpdesk is an important resource providing expectant mothers and mothers of infants with an interactive option for accessing MCH-related information—above that included in the standard MomConnect messages—and advances achievement of the health goals of the MomConnect programme.

Semantic Analysis of Open Source Data for Syndromic Surveillance

ObjectiveThe objective of this analysis is to leverage recent advances innatural language processing (NLP) to develop new methods andsystem capabilities for processing social media (Twitter messages)for situational awareness (SA), syndromic surveillance (SS), andevent-based surveillance (EBS). Specifically, we evaluated the useof human-in-the-loop semantic analysis to assist public health (PH)SA stakeholders in SS and EBS using massive amounts of publiclyavailable social media data.IntroductionSocial media messages are often short, informal, and ungrammatical.They frequently involve text, images, audio, or video, which makesthe identification of useful information difficult. This complexityreduces the efficacy of standard information extraction techniques1.However, recent advances in NLP, especially methods tailoredto social media2, have shown promise in improving real-time PHsurveillance and emergency response3. Surveillance data derived fromsemantic analysis combined with traditional surveillance processeshas potential to improve event detection and characterization. TheCDC Office of Public Health Preparedness and Response (OPHPR),Division of Emergency Operations (DEO) and the Georgia TechResearch Institute have collaborated on the advancement of PH SAthrough development of new approaches in using semantic analysisfor social media.MethodsTo understand how computational methods may benefit SS andEBS, we studied an iterative refinement process, in which the datauser actively cultivated text-based topics (“semantic culling”) in asemi-automated SS process. This ‘human-in-the-loop’ process wascritical for creating accurate and efficient extraction functions in large,dynamic volumes of data. The general process involved identifyinga set of expert-supplied keywords, which were used to collect aninitial set of social media messages. For purposes of this analysisresearchers applied topic modeling to categorize related messages intoclusters. Topic modeling uses statistical techniques to semanticallycluster and automatically determine salient aggregations. A user thensemantically culled messages according to their PH relevance.In June 2016, researchers collected 7,489 worldwide English-language Twitter messages (tweets) and compared three samplingmethods: a baseline random sample (C1, n=2700), a keyword-basedsample (C2, n=2689), and one gathered after semantically cullingC2 topics of irrelevant messages (C3, n=2100). Researchers utilizeda software tool, Luminoso Compass4, to sample and perform topicmodeling using its real-time modeling and Twitter integrationfeatures. For C2 and C3, researchers sampled tweets that theLuminoso service matched to both clinical and layman definitions ofRash, Gastro-Intestinal syndromes5, and Zika-like symptoms. Laymanterms were derived from clinical definitions from plain languagemedical thesauri. ANOVA statistics were calculated using SPSSsoftware, version. Post-hoc pairwise comparisons were completedusing ANOVA Turkey’s honest significant difference (HSD) test.ResultsAn ANOVA was conducted, finding the following mean relevancevalues: 3% (+/- 0.01%), 24% (+/- 6.6%) and 27% (+/- 9.4%)respectively for C1, C2, and C3. Post-hoc pairwise comparison testsshowed the percentages of discovered messages related to the eventtweets using C2 and C3 methods were significantly higher than forthe C1 method (random sampling) (p&lt;0.05). This indicates that thehuman-in-the-loop approach provides benefits in filtering socialmedia data for SS and ESB; notably, this increase is on the basis ofa single iteration of semantic culling; subsequent iterations could beexpected to increase the benefits.ConclusionsThis work demonstrates the benefits of incorporating non-traditional data sources into SS and EBS. It was shown that an NLP-based extraction method in combination with human-in-the-loopsemantic analysis may enhance the potential value of social media(Twitter) for SS and EBS. It also supports the claim that advancedanalytical tools for processing non-traditional SA, SS, and EBSsources, including social media, have the potential to enhance diseasedetection, risk assessment, and decision support, by reducing the timeit takes to identify public health events.

Statistical and Qualitative Analyses of Students� Answers to a Constructed Response Test of Science Inquiry Knowledge

Objective: We report on a comparative study of the language used by middle school students in their answers to a constructed response test of science inquiry knowledge. Background: Text analyses using statistical models have been conducted across a number of disciplines to identify topics in a journal, to extract topics in Twitter messages, and to investigate political preferences. In education, relatively few studies have analyzed the text of students’ written answers to investigate topics underlying the answers. Methodology: Two types of linguistic analysis were compared to investigate their utility in understanding students’ learning of scientific investigation practices. A statistical method, latent Dirichlet allocation (LDA), was used to extract topics from the texts of student responses. In the LDA model, topics are viewed as multinomial distributions over the vocabulary of documents. These topics were examined for content and used to characterize student responses on the constructed response items. The change from pre-test to post-test in proportions of use of each of the topics was related to students’ learning. Next, a qualitative method, systemic functional linguistic (SFL) analysis, was used to analyze the text of student responses on the same test of science inquiry knowledge. Student assessments were analyzed for two linguistic features that are important for convincing scientific communication: technical vocabulary usage and high lexical density. In this way, we investigated whether human judgement regarding the changes observed from texts based on the SFL framework agreed with the inference regarding the changes observed from the texts through LDA. Research questions: Two research questions were investigated in this study: (1) What do the LDA and SFL analyses tell us about students’ answers? (2) What are the similarities and differences of the two analyses? Data: The data for this study were taken from an NSF-funded host study on teaching science inquiry skills to middle school students who were a mix of both native English speakers and English-language learners. The primary objective was to enable participants to learn to take ownership of scientific language through the use of language-rich science investigation practices. The LDA analysis used a sample of 252 students’ pre-and post-assessments. The SFL analysis used a second sample of 90 students’ pre- and post-assessments. Results: In the LDA analysis, three topics were detected in student responses: “preponderance of everyday language (Topic 1),” “preponderance of general academic language (Topic 2),” and “preponderance of discipline-specific language (Topic 3).” Students’ use of topics changed from pre-test to post-test. Students on the post-test tended to have higher proportions of Topic 3 than students on the pre-test. In the SFL analysis, students tended to use more technical vocabulary and have higher lexical density in their written responses on the post-test than on the pre-test. Discussion: Results from the LDA and SFL analyses suggest that students responded using more discipline-specific language on the post-test than on the pre-test. In addition, the results of the two linguistic features from the SFL analysis, technical vocabulary usage and lexical density, were compared with the results from the LDA analysis. • Conclusion: Results of the LDA and SFL analyses were consistent with each other and clearly showed that students improved in their ability to use the discipline-specific and academic terminology of the language of scientific communication.

Instant Message Classification in Finnish Cyber Security Themed Free-Form Discussion

Instant messaging enables rapid collaboration between professionals during cyber security incidents. However, monitoring discussion manually becomes challenging as the number of communication channels increases. Failure to identify relevant information from the free-form instant messages may lead to reduced situational awareness. In this paper, the problem was approached by developing a framework for classification of instant message topics of cyber security-themed discussion in Finnish. The program utilizes open source software components in morphological analysis, and subsequently converts the messages into Bag-of-Words representations before classifying them into predetermined incident categories. We compared support vector machines (SVM), multinomial naive Bayes, and complement naive Bayes (CNB) classification methods with five-fold cross-validation. A combination of SVM and CNB achieved classification accuracy of over 85 %, while multiclass SVM achieved 87 % accuracy. The implemented program recognizes cyber security-related messages in IRC chat rooms and categorizes them accordingly.

How people can become persuaded by weak messages presented by credible communicators: Not all sleeper effects are created equal

The sleeper effect has been proposed to describe temporal changes in persuasion for messages associated with noncredible sources. The present research introduces a new kind of sleeper effect denoting increases in persuasion for weak messages associated with credible sources. This effect of the source was hypothesized to derive from attending to the message source rather than the message arguments and reconstructing delayed attitudes primarily on the basis of the source information. Findings from three experiments revealed that when the focus of attention was the communicator, there was a sleeper effect for the source. Specifically, during the time between an immediate follow up and a delayed follow up, persuasion increased when credible sources presented weak arguments. In contrast, when the focus of attention was the message arguments, a traditional sleeper effect emerged. That is, persuasion increased when strong arguments were presented by a noncredible communicator. These effects were mediated by relative recall of arguments versus source attributes and replicated with different message topics and lengths of delay.

Exploring Division-I Student-Athletes’ Memorable Messages From Their Anticipatory Socialization

This study explored the socialization of 118 Division-I student-athletes via the topics of memorable messages that they received prior to their arrival on campus. Ten topics were identified (i.e., desirable attitudes, hard work, physical skill or ability, opportunities, pride, inclusion, challenges, athletes as symbols, the importance of education, and the duration of college athletics) using first cycle coding and were subsequently categorized as either addressing the characteristics or experiences of collegiate student-athletes using second cycle coding. These findings continue to demonstrate that characteristics and experiences associated with roles are prevalent within athletes’ memorable messages but also highlight the inherent ambiguity and the contradictions regarding how to use these messages and balance the dual roles of being a student-athlete. This study provides a novel communicative lens for understanding athlete socialization but underscores the need to recognize receivers’ processing and application of memorable messages.

Predictive Validity of an Empirical Approach for Selecting Promising Message Topics: A Randomized-Controlled Study.

Several message topic selection approaches propose that messages based on beliefs pretested and found to be more strongly associated with intentions will be more effective in changing population intentions and behaviors when used in a campaign. This study aimed to validate the underlying causal assumption of these approaches which rely on cross-sectional belief-intention associations. We experimentally tested whether messages addressing promising themes as identified by the above criterion were more persuasive than messages addressing less promising themes. Contrary to expectations, all messages increased intentions. Interestingly, mediation analyses showed that while messages deemed promising affected intentions through changes in targeted promising beliefs, messages deemed less promising also achieved persuasion by influencing nontargeted promising beliefs. Implications for message topic selection are discussed.

정치인들의 선거 캠페인 수단으로서의 SNS 활용: 2015년 4·29 재·보궐선거를 중심으로

Considerable research over the years has been devoted to ascertaining the impact of social media on political settings.In recent days, Social Network Sites (SNS) such as Facebook allowed users to share their political beliefs, support specific candidates, and interact with others on political issues. This study examines the role of SNS as the means of political campaign. The study tasks the case of the 2015’sspecial election, Seoul Korea. The analysis aims to identify how candidates use Facebook or Twitter to interact with voters by applying functional theory of political campaign discourse developed by Benoit. In this study, we analyzed the candidates’ SNS messages in terms of political behavior such as self-expression, informing policy, asking voters to participate in political events. Among them the results indicated that two candidates, Jung, Dong Young and Byun, Hee Jae, both of them used SNS to express themselves the most. The study also found that two candidates used mainly the strategy called ‘acclaim’ which praises their own strengths. In terms of topics of SNS messages (policy versus character) there was different between two candidates. Jung, sent message in relation to ‘character’ the most, while Byun contained ‘policy’ message on SNS the most. Based on these findings implications and directions for future studies are discussed. ☞ keyword : SNS, election campaign, special election, political communication

Personality as a metric for topic models on social networks

As user-generated content suffers from severe problems of data sparseness, many topic models designed for social network are proposed. Without a unified metric, the methods to weigh topics are mixed. Since topic models like Dirichlet Allocation (LDA) can summarize the main information of news articles, topics of short messages on social networking websites should reveal key features of the authors of messages. Personality is the natural characteristic of human. Past work on personality identification has shown that the words people say on social network can reveal people's personality but none of them compare the effects of different topic model tactics on personality identifying. We run LDA and one of its variant (Twitter-LDA) on real social network data (Facebook status messages) then use the topics distribution as features to identify pre-labelled Big-Five personality traits. The results demonstrate that the likelihood of personality as a metric can discover more features of topic models than the model designer said. Furthermore, our research add values to personality identification.

Tweeting as Health Communication: Health Organizations’ Use of Twitter for Health Promotion and Public Engagement

This study examined how major health organizations use Twitter for disseminating health information, building relationships, and encouraging actions to improve health. The sampled organizations were the American Heart Association, American Cancer Society, and American Diabetes Association. A content analysis was conducted on 1,583 tweets to examine these organizations’ use of Twitter's interactive features and to understand the message functions and topics of their tweets. The numbers of retweets and favorites were also measured as engagement indicators and compared by different message functions. The results revealed that all of the organizations posted original tweets most, but they differed in the degree to which they used the retweet and reply functions. Hashtags and hyperlinks were the most frequently used interactive tools. The majority of the tweets were about organization-related topics, whereas personal health–related tweets represented a relatively small portion of the sample. Followers were most likely to like and retweet personal health action-based messages.

Comparison of beliefs about e-cigarettes’ harms and benefits among never users and ever users of e-cigarettes

IntroductionE-cigarette use is rapidly increasing, especially among youth and young adults. We need to learn what factors are associated with uptake in e-cigarettes. One important set of predictors is beliefs about e-cigarettes’ potential harms and benefits. MethodsOnline survey data were collected in July, 2014 from 527 U.S. adults from a nationally representative online panel (KnowledgePanel) who reported being aware of e-cigarettes. Participants were asked to rate 7 statements related to e-cigarettes harms or benefits (e.g., breathing vapors from other people's e-cigarettes is harmful to my health; vaping or using e-cigarettes can help people quit smoking regular cigarettes completely). Responses were categorized into agree, disagree, or no opinion. We compared the proportions of agreement between respondents who ever used e-cigarettes and those who had never used. Multinomial logistic regression was used to predict agree or no opinion versus disagree (base outcome) for each belief. Relative risk ratios (RRRs) are reported. The analyses were completed in December, 2014 and were weighted to match the general U.S. adult population. ResultsAgreement across the 7 beliefs ranged from 33% (vaping can help people quit smoking) to 56% (e-cigarettes make smoking look more acceptable to youth). Ever use of e-cigarettes was associated with lower relative risk of agreeing with statements about potential harms and higher relative risk of agreeing with statements about benefits (versus disagreeing) compared with never users. DiscussionThese findings provide timely data on beliefs about e-cigarettes between e-cigarette users and non-users to inform potential message topics for health campaign interventions.

TOPTRAC: Topical Trajectory Pattern Mining.

With the increasing use of GPS-enabled mobile phones, geo-tagging, which refers to adding GPS information to media such as micro-blogging messages or photos, has seen a surge in popularity recently. This enables us to not only browse information based on locations, but also discover patterns in the location-based behaviors of users. Many techniques have been developed to find the patterns of people's movements using GPS data, but latent topics in text messages posted with local contexts have not been utilized effectively. In this paper, we present a latent topic-based clustering algorithm to discover patterns in the trajectories of geo-tagged text messages. We propose a novel probabilistic model to capture the semantic regions where people post messages with a coherent topic as well as the patterns of movement between the semantic regions. Based on the model, we develop an efficient inference algorithm to calculate model parameters. By exploiting the estimated model, we next devise a clustering algorithm to find the significant movement patterns that appear frequently in data. Our experiments on real-life data sets show that the proposed algorithm finds diverse and interesting trajectory patterns and identifies the semantic regions in a finer granularity than the traditional geographical clustering methods.

How different countries addressed the sudden growth of e-cigarettes in an online tobacco control community

ObjectiveThe sudden growth of e-cigarettes over the last decade has forced advocates and critics scrambling to bolster support for their respective sides. Bridging the divide in geographic barriers, social networking...

A linguistic approach for determining the topics of Spanish Twitter messages

The vast number of opinions and reviews provided in Twitter is helpful in order to make interesting findings about a given industry, but given the huge number of messages published every day, it is important to detect the relevant ones. In this respect, the Twitter search functionality is not a practical tool when we want to poll messages dealing with a given set of general topics. This article presents an approach to classify Twitter messages into various topics. We tackle the problem from a linguistic angle, taking into account part-of-speech, syntactic and semantic information, showing how language processing techniques should be adapted to deal with the informal language present in Twitter messages. The TASS 2013 General corpus, a collection of tweets that has been specifically annotated to perform text analytics tasks, is used as the dataset in our evaluation framework. We carry out a wide range of experiments to determine which kinds of linguistic information have the greatest impact on this task and how they should be combined in order to obtain the best-performing system. The results lead us to conclude that relating features by means of contextual information adds complementary knowledge over pure lexical models, making it possible to outperform them on standard metrics for multilabel classification tasks.

Police message diffusion on Twitter: analysing the reach of social media communications

Social media are becoming increasingly important for communication between government organisations and citizens. Although research on this issue is expanding, the structure of these new communication patterns is still poorly understood. This study contributes to our understanding of these new communication patterns by developing an explanatory model of message diffusion on social media. Messages from 964 Dutch police force Twitter accounts are analysed using trace data drawn from the Twitter™ API to explain why certain police tweets are forwarded and others are not. Based on an iterative human calibration procedure, message topics were automatically coded based on customised lexicons. A principal component analysis of message characteristics generated four distinct patterns of use in (in)personal communication and new/versus reproduced content. Message characteristics were combined with user characteristics in a multilevel logistic general linear model. Our main results show that URLs or use of informal communication increases chances of message forwarding. In addition, contextual factors such as user characteristics impact diffusion probability. Recommendations are discussed for further research into authorship styles and their implications for social media message diffusion. For the police and other government practitioners, a list of recommendation about how to reach a larger number of citizens through social media communications is presented.