Social-Cyber Maneuvers Analysis During the COVID-19 Vaccine Initial Rollout (Preprint)

2021 ◽  
Author(s):  
Janice Blane ◽  
Daniele Bellutta ◽  
Kathleen M Carley
Keyword(s):  
Social Media ◽  
Comparative Analysis ◽  
Online Community ◽  
Effective Means ◽  
Initial Distribution ◽  
Community Response ◽  
Twitter Data ◽  
Linguistic Cues ◽  
Entire Network ◽  
Over Time

BACKGROUND During the period surrounding the approval and initial distribution of Pfizer-BioNTech’s COVID-19 vaccine, many users took to social media to voice their opinions on the vaccine. They formed pro- and anti-vaccination groups and influenced behaviors to vaccinate or not to vaccinate. The methods of persuasion and manipulation for convincing audiences online can be characterized under a framework for social-cyber maneuvers known as the BEND maneuvers. Previous studies have been conducted on the spread of COVID-19 vaccine disinformation. However, none have used a process that conducts comparative analyses over time on both community stances and the competing techniques of manipulating both the narrative and network structure to persuade target audiences. OBJECTIVE This study aimed to understand community response to vaccination by dividing Twitter data from the initial Pfizer-BioNTech COVID-19 vaccine rollout into pro-vaccine and anti-vaccine stances, identifying key actors and groups, and evaluating how the different communities use social-cyber maneuvers, or BEND maneuvers, to influence their target audiences and the network as a whole. METHODS COVID-19 Twitter vaccine data was collected using the Twitter API for one-week periods before, during, and six weeks after the initial Pfizer-BioNTech rollout (December 2020-January 2021). Bot identifications and linguistic cues were derived for users and tweets, respectively, to use as metrics for evaluating social-cyber maneuvers. ORA-PRO software was then used to separate the vaccine data into pro-vaccine and anti-vaccine communities and facilitate identifying key actors, groups, and BEND maneuvers for a comparative analysis between each community and the entire network. RESULTS Both the pro-vaccine and anti-vaccine communities used combinations of the 16 BEND maneuvers to persuade their target audiences of their particular stances. Our analysis showed how each side attempted to build its own community while simultaneously narrowing and neglecting the opposing community. Pro-vaccine users primarily used positive maneuvers such as excite and explain messages to encourage vaccination and backed leaders within their group. In contrast, anti-vaccine users relied on negative maneuvers to dismay and distort messages with narratives on side effects and death and attempted to neutralize the effectiveness of the leaders within the pro-vaccine community. Furthermore, nuking through platform policies showed to be effective in reducing the size of the anti-vaccine online community and the quantity of anti-vaccine messages. CONCLUSIONS Social media continues to be a domain for manipulating beliefs and ideas. These conversations can ultimately lead to real-world actions such as to vaccinate or not to vaccinate against COVID-19. Moreover, social media policies should be further explored as an effective means for curbing disinformation and misinformation online. CLINICALTRIAL Not applicable

Dynamics of convergence behaviour in social media crisis communication – a complexity perspective

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Milad Mirbabaie ◽  
Stefan Stieglitz ◽  
Felix Brünker
Keyword(s):  
Social Media ◽  
Emergency Management ◽  
Crisis Communication ◽  
Design Methodology ◽  
Content Type ◽  
The Public ◽  
Analysis Techniques ◽  
Twitter Data ◽  
Convergence Behaviour ◽  
Over Time

PurposeThe purpose of this study is to investigate communication on Twitter during two unpredicted crises (the Manchester bombings and the Munich shooting) and one natural disaster (Hurricane Harvey). The study contributes to understanding the dynamics of convergence behaviour archetypes during crises.Design/methodology/approachThe authors collected Twitter data and analysed approximately 7.5 million relevant cases. The communication was examined using social network analysis techniques and manual content analysis to identify convergence behaviour archetypes (CBAs). The dynamics and development of CBAs over time in crisis communication were also investigated.FindingsThe results revealed the dynamics of influential CBAs emerging in specific stages of a crisis situation. The authors derived a conceptual visualisation of convergence behaviour in social media crisis communication and introduced the terms hidden and visible network-layer to further understanding of the complexity of crisis communication.Research limitations/implicationsThe results emphasise the importance of well-prepared emergency management agencies and support the following recommendations: (1) continuous and (2) transparent communication during the crisis event as well as (3) informing the public about central information distributors from the start of the crisis are vital.Originality/valueThe study uncovered the dynamics of crisis-affected behaviour on social media during three cases. It provides a novel perspective that broadens our understanding of complex crisis communication on social media and contributes to existing knowledge of the complexity of crisis communication as well as convergence behaviour.

scholarly journals Potential social media campaign challenge towards physical activity in modern era

2018 ◽  
pp. 14
Author(s):  
Nurlienda Hasanah ◽  
Hafidhotun Nabawiyah
Keyword(s):  
Physical Activity ◽  
Social Media ◽  
Online Community ◽  
Community Response ◽  
Media Campaign ◽  
Health Campaign ◽  
Theoretical Understanding ◽  
The Social ◽  
Activity Awareness ◽  
Modern Era

The phenomenon of social media interactions can influence awareness behavior into multiple layers of firm-initiated, communitiated actions and provides a theoretical understanding of what firms and community accomplish using social media.The aim of this study isto explore community response of “promkes.net” as a program about “melawan mager” (sedentary lifestyle challenge) by social media campaign using hashtag #7hariMelawanMager. This study was a literature review, using supported document and mini-survey about the challenge at social media campaign using Instagram story survey and Facebook polling in 1 days and also challenge’s founder interview.As the result in three weeks after the launching challenge, there were 41 posting in Instagram with 9 people completed the #7harimelawanmager challenge and 33 posting in  FB with 13 people completed the challenge via FB. The social media challenge raising physical activity awareness from the society, yet there were 17 people who fulfill the promkes_net Instagram story survey and 5 of 15 people did not know about the campaign via FB’s author polling.At the end of review, there are 98 people and still counting who join the challenges. Online community based were built on Facebook for sharing & motivating each other, starting from social media challenges. Health campaign by social media using hashtag #7harimelawanmager potentially raising physical activity awareness but this campaign should be sustainable and have any improvement in several periods. Furthermore, society can become more aware what happen and participate in this issue.

scholarly journals Sentiment urgency emotion conversion over time for business intelligence

2020 ◽  
Vol 16 (5) ◽  
pp. 519-528
Author(s):  
Tariq Soussan ◽  
Marcello Trovati
Keyword(s):  
Social Media ◽  
Online Community ◽  
Marketing Plan ◽  
Content Type ◽  
Emotion Conversion ◽  
Twitter Account ◽  
General Business ◽  
The Uk ◽  
Media Data ◽  
Over Time

Purpose Social media has become a vital part of any institute’s marketing plan. Social networks benefit businesses by allowing them to interact with their clients, grow brand exposure through offers and promotions and find new leads. It also offers vital information concerning the general emotions and sentiments directly connected to the welfare and security of the online community involved with the brand. Big organizations can make use of their social media data to generate planned and operational decisions. This paper aims to look into the conversion of sentiments and emotions over time. Design/methodology/approach In this work, a model called sentiment urgency emotion detection (SUED) from previous work will be applied on tweets from two different periods of time, one before the start of the COVID-19 pandemic and the other after it started to monitor the conversion of sentiments and emotions over time. The model has been trained to improve its accuracy and F1 score so that the precision and percentage of correctly predicted texts is high. This model will be tuned to improve results (Soussan and Trovati, 2020a; Soussan and Trovati, 2020b) and will be applied on a general business Twitter account of one of the largest chains of supermarkets in the UK to be able to see what sentiments and emotions can be detected and how urgent they are. Findings This will show the effect of COVID-19 pandemic on the conversions of the sentiments, emotions and urgencies of the tweets. Originality/value Sentiments will be compared between the two periods to evaluate how sentiments and emotions vary over time taking into consideration the COVID-19 as an affective factor. In addition, SUED will be tuned to enhance results and the knowledge that is mined when turning data into decisions is crucial because it will aid stakeholders handling the institute to evaluate the topics and issues that were mostly emphasized.

scholarly journals We grow by Retweeting and Following: Examining the Pro-Eating Disorders community on Twitter via #Proana (Preprint)

2020 ◽  
Author(s):  
Suku SUKUNESAN
Keyword(s):  
Social Media ◽  
Eating Disorders ◽  
Online Community ◽  
Weight Control ◽  
Linear Models ◽  
Reciprocal Effect ◽  
Final Sample ◽  
Social Media Platforms ◽  
Twitter Users ◽  
Over Time

BACKGROUND There is increasing concern around communities which promote eating disorders (Pro-ED) on social media sites through messages and images which encourage dangerous weight control behaviours. These communities share group identity formed through interactions between members and can involve the exchange of ‘tips’, restrictive dieting plans, extreme exercise plans and motivating imagery of thin bodies. Unlike Instagram, Facebook or Tumblr, the absence of adequate policy to moderate Pro-ED content on Twitter presents a unique space Pro-ED community to freely communicate. While recent research have identified terms, themes and common lexicon used within the Pro-ED online community very few have been longitudinal. It is important to focus upon the engagement of Pro-ED online communities over time to further understand how members interact and stay connected, which is currently lacking. OBJECTIVE The purpose of this study was to explore beyond the common messages of Pro-ED on Twitter to understand how Pro-ED communities get traction over time by using the hashtag considered to symbolise the Pro-ED movement, #proana. Our focus was to collect longitudinal data to gain further understanding on the engagement of Pro-ED communities on Twitter. METHODS Descriptive statistics were used to identify the preferred tweeting style of Twitter users (either as mentioning another user in a tweet, or as an individual tweet to oneself, commonly referred to as ‘self-directed’) as well as their most frequently used hashtag, in addition to #proana. A series of Mann Whitney U tests were then conducted to compare preferred posting style across number of followed, followers, tweets and favourites. This was followed by Linear models using a forward step-wise approach, were applied for Pro-ED Twitter users to examine the factors associated with their number of followers. RESULTS This study consisted of 11,620 Pro-ED Twitter accounts who posted using the hashtag #proana between September 2015 and July 2018. These profiles then underwent before a two-step inclusion / exclusion criteria screen to reach the final sample of 967 profiles. Over 90% (10,484) of the profiles were found to have less than 6 tweets within the 34 months period. Most of the users were identified as preferring a mentioning style of tweeting (74.3%) over self-directed styles (25.7%). Further, #proana and #thinspo were used interchangeably to propagate shared themes and there was a reciprocal effect between followers and followed. CONCLUSIONS Our analysis showed that the number of accounts followed and number of Pro-ED tweets posted were significant predictors for the number of followers a user compared to likes. Our results could potentially be useful to social media platforms to understand which features could help or otherwise in curtailing spread of ED messages and activity. Our findings also show that Pro-ED communities are transient in nature engaging in superficial discussion threads but resilient, emulating cybersectarian behaviour.

Exploring the Use of Machine Learning to Automate the Qualitative Coding of Church-related Tweets

2020 ◽  
Vol 14 (2) ◽  
pp. 140-159
Author(s):  
Anthony-Paul Cooper ◽  
Emmanuel Awuni Kolog ◽  
Erkki Sutinen
Keyword(s):  
Machine Learning ◽  
Online Community ◽  
High Volume ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Social Media Data ◽  
Twitter Data ◽  
Resource Intensity ◽  
Media Data ◽  
Better Than

This article builds on previous research around the exploration of the content of church-related tweets. It does so by exploring whether the qualitative thematic coding of such tweets can, in part, be automated by the use of machine learning. It compares three supervised machine learning algorithms to understand how useful each algorithm is at a classification task, based on a dataset of human-coded church-related tweets. The study finds that one such algorithm, Naïve-Bayes, performs better than the other algorithms considered, returning Precision, Recall and F-measure values which each exceed an acceptable threshold of 70%. This has far-reaching consequences at a time where the high volume of social media data, in this case, Twitter data, means that the resource-intensity of manual coding approaches can act as a barrier to understanding how the online community interacts with, and talks about, church. The findings presented in this article offer a way forward for scholars of digital theology to better understand the content of online church discourse.

Utilizing Twitter Data Analysis and Deep Learning to Identify Drug Use (Preprint)

2019 ◽  
Author(s):  
Joseph Tassone ◽  
Peizhi Yan ◽  
Mackenzie Simpson ◽  
Chetan Mendhe ◽  
Vijay Mago ◽  
...  
Keyword(s):  
Social Media ◽  
Logistic Regression ◽  
Deep Learning ◽  
Decision Tree ◽  
Semantic Meaning ◽  
Predictive Capability ◽  
Logistic Regression Models ◽  
Twitter Data ◽  
Data Points ◽  
Positive Classification

BACKGROUND The collection and examination of social media has become a useful mechanism for studying the mental activity and behavior tendencies of users. OBJECTIVE Through the analysis of a collected set of Twitter data, a model will be developed for predicting positively referenced, drug-related tweets. From this, trends and correlations can be determined. METHODS Twitter social media tweets and attribute data were collected and processed using topic pertaining keywords, such as drug slang and use-conditions (methods of drug consumption). Potential candidates were preprocessed resulting in a dataset 3,696,150 rows. The predictive classification power of multiple methods was compared including regression, decision trees, and CNN-based classifiers. For the latter, a deep learning approach was implemented to screen and analyze the semantic meaning of the tweets. RESULTS The logistic regression and decision tree models utilized 12,142 data points for training and 1041 data points for testing. The results calculated from the logistic regression models respectively displayed an accuracy of 54.56% and 57.44%, and an AUC of 0.58. While an improvement, the decision tree concluded with an accuracy of 63.40% and an AUC of 0.68. All these values implied a low predictive capability with little to no discrimination. Conversely, the CNN-based classifiers presented a heavy improvement, between the two models tested. The first was trained with 2,661 manually labeled samples, while the other included synthetically generated tweets culminating in 12,142 samples. The accuracy scores were 76.35% and 82.31%, with an AUC of 0.90 and 0.91. Using association rule mining in conjunction with the CNN-based classifier showed a high likelihood for keywords such as “smoke”, “cocaine”, and “marijuana” triggering a drug-positive classification. CONCLUSIONS Predictive analysis without a CNN is limited and possibly fruitless. Attribute-based models presented little predictive capability and were not suitable for analyzing this type of data. The semantic meaning of the tweets needed to be utilized, giving the CNN-based classifier an advantage over other solutions. Additionally, commonly mentioned drugs had a level of correspondence with frequently used illicit substances, proving the practical usefulness of this system. Lastly, the synthetically generated set provided increased scores, improving the predictive capability. CLINICALTRIAL None

Historical Representations and Recent Changes

2018 ◽  
Author(s):  
Jürgen Schaflechner
Keyword(s):  
Comparative Analysis ◽  
South Asia ◽  
Oral Histories ◽  
Detailed History ◽  
Modern Times ◽  
History Of ◽  
Historical Representations ◽  
The Rich ◽  
Many Sources ◽  
Over Time

Chapter 3 introduces the tradition of ritual journeys and sacred geographies in South Asia, then hones in on a detailed history of the grueling and elaborate pilgrimage attached to the shrine of Hinglaj. Before the construction of the Makran Coastal Highway the journey to the Goddess’s remote abode in the desert of Balochistan frequently presented a lethally dangerous undertaking for her devotees, the hardships of which have been described by many sources in Bengali, Gujarati, Hindi, Sindhi, and Urdu. This chapter draws heavily from original sources, including travelogues and novels, which are supplanted with local oral histories in order to weave a historical tapestry that displays the rich array of practices and beliefs surrounding the pilgrimage and how they have changed over time. The comparative analysis demonstrates how certain motifs, such as austerity (Skt. tapasyā), remain important themes within the whole Hinglaj genre even in modern times while others have been lost in the contemporary era.

scholarly journals Late-Holocene diatom community response to climate driven chemical changes in a small, subarctic lake, Northwest Territories, Canada

The Holocene ◽  
2021 ◽  
pp. 095968362110032
Author(s):  
Paul B Hamilton ◽  
Scott J Hutchinson ◽  
R Timothy Patterson ◽  
Jennifer M Galloway ◽  
Nawaf A Nasser ◽  
...  
Keyword(s):  
Iron Age ◽  
Biological Diversity ◽  
Regional Climate ◽  
Natural Aging ◽  
Community Response ◽  
Warm Period ◽  
Ice Age ◽  
Diatom Assemblages ◽  
Subarctic Lake ◽  
Over Time

The paleolimnological record of diatoms and climate, spanning the last 2800 years, was investigated in a small subarctic lake (Pocket Lake) that from AD 1948 to 2004 was contaminated by gold smelting waste. An age-depth model was constructed using a combination of 210Pb, 14C, and tephra to determine a 2800 year history of lake ontogeny (natural aging), biological diversity, and regional climate variability. Diatoms form six strong paleoecological assemblages over time in response to changes in local hydrological and sedimentological conditions (including metals). Selected environmental variables explained 28.8% of the variance in the diatom assemblages, with Fe, Ca, and sediment end member distribution being important indicators. The diatom assemblages correlated to the Iron Age Cold Epoch (2800–2300 cal BP), Roman Warm Period (2250–1610 cal BP), Dark Age Cold Period (1500–1050 cal BP), Medieval Climate Anomaly (ca. 1100–800 cal BP), and the Little Ice Age (800–200 cal BP). The disappearance of Staurosira venter highlights the change from the Iron Age Cold Epoch to the Roman Warm Period. After deposition of the White River Ash (833–850 CE; 1117–1100 cal BP), transition to circumneutral conditions was followed in tandem by a transition to planktic influenced communities. Ten discrete peaks of Cu, Pb, and Zn were observed and attributed to soluble mobility from catchment soils through enhanced seepage and spring snowmelt. The prominent metal spikes were aligned with increases in Brachysira neoexilis. Downward mobilization of arsenic and antimony from contaminated surficial sediments highlight the problem of post depositional industrial contamination of paleosediments. Results demonstrate that paleoclimatic changes in the region, modulated by solar radiation, impacted temperature and precipitation in the lake catchment, influencing temporal shifts in diatom ecology. Changes in diatom taxa richness provided valuable information on the relative influence of water quality (planktic taxa) and sediment input (benthic taxa). The diatom assemblage succession also provides evidence that natural aging over time has played a role in the ecological evolution of the lake.

scholarly journals Analysis of Community Response to Disasters through Twitter Social Media

2021 ◽  
Vol 1807 (1) ◽  
pp. 012033
Author(s):  
Apri Junaidi ◽  
Iqsyahiro Kresna A ◽  
Richki Hardi
Keyword(s):  
Social Media ◽  
Community Response
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