detecting opinion leaders and trends in online social networks pdf

Detecting Opinion Leaders And Trends In Online Social Networks Pdf

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Opinion leadership is leadership by an active media user who interprets the meaning of media messages or content for lower-end media users. Typically the opinion leader is held in high esteem by those who accept their opinions. Opinion leadership comes from the theory of two-step flow of communication propounded by Paul Lazarsfeld and Elihu Katz.

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Social media has reshaped individual and institutional communication. The unrestricted access to spontaneous views and opinions of society can enrich the evaluation of healthcare interventions. Antimicrobial resistance has been identified as a global threat to health requiring collaboration between clinicians and healthcare users. We sought to explore events and individuals influencing the discourse about antibiotics on Twitter. A web-based tool www.

Opinion leadership

Social media has reshaped individual and institutional communication. The unrestricted access to spontaneous views and opinions of society can enrich the evaluation of healthcare interventions. Antimicrobial resistance has been identified as a global threat to health requiring collaboration between clinicians and healthcare users. We sought to explore events and individuals influencing the discourse about antibiotics on Twitter. A web-based tool www.

Activity peaks message frequency over three times that of baseline were analysed to identify events leading to the increase. The CMO report in March reached an estimated worldwide audience of 20 million users in a single day. However, the frequency of antibiotic Tweets returned to basal levels within 48 h of all four peaks in activity. Institutional events can rapidly amplify antibiotic discussions on social media, but their short lifespan may hinder their public impact.

Multipronged strategies may be required to prolong responses. Developing methods to refine social media monitoring to evaluate the impact and sustainability of societal engagement in the antimicrobial resistance agenda remains essential. The development of social media and social networks has provided unprecedented communication opportunities between individuals, companies and organizations.

For example, public health commissioners and practitioners have been able to find feedback that has traditionally been difficult to obtain from populations where policies or interventions have been implemented. These messages are time-stamped and, if enabled, pinpoint the geographical location of the user at the time of posting. However, not all demographic groups are equally represented and current users are mainly young, adult, middle-class males.

This direct access helps to explain the popularity of the platform. The use of web analytic measures to rank users according to their online social influence, 8 , 9 although debated in terms of accuracy, does allow for the identification of many key influencers within the crowd of individuals and organizations, a useful feature that could be used to maximize the impact of a given health campaign or message. The vast quantity of data generated in real time by users of the service has increasingly been exploited for healthcare and public health analysis.

For example, there have been experiences in epidemic intelligence and surveillance epidemiology, 10 — 12 including disease activity, 13 , 14 understanding public perceptions and attitudes towards public health campaigns and measures such as vaccination, 3 , 4 , 15 and as a tool of public health education and health promotion. Healthcare institutions do not seem to be taking full advantage of the educational potential of Twitter.

Thus, the potential utilization of this communication channel as a means of engaging in meaningful dialogue with citizens as a way of further promoting public health issues has yet to be realized. Amongst current global public health problems, antimicrobial resistance has been cited by the WHO as one of the top three threats to human health.

There are annual awareness campaigns in many countries and more recently the UK Chief Medical Officer CMO coauthored a short book explaining the tangible dangers posed by resistant bacteria. Within this context of societal engagement there is potential for social media platforms to be used to raise awareness of growing antimicrobial resistance, to monitor the impact of campaigns and to seek better understanding of the perspectives of individuals on the use of antibiotics.

To date, there has been limited analysis of how and when people talk about antibiotics on social media. A web-based tool Topsy, www. Topsy is one of the few companies with access to all historic Tweets sent since the launch of Twitter in Next, we calculated the daily average of antibiotic Tweets and used that average as the baseline of activity. Single day peaks, defined as those with a frequency over three times that of baseline, were identified from a graphical representation of activity generated using Topsy.

The individual Tweets contributing to these peaks were analysed to identify the presence of particular events leading to the increase. In addition, information on the geographical distribution of users sending antibiotic Tweets on single day peaks, and on basal days, was sought from the analysis tools within Topsy.

On average, Tweets about antibiotics were posted per day. Such reach was achieved via traditional news outlets such as the British Broadcasting Corporation www. Only two individuals featured amongst the top 10 disseminators.

For all the peaks identified, and the potential audience reached, the daily number of antibiotic Tweets returned to the baseline frequency within 24—48 h. Reproduced with permission from Topsy. Although messages predominantly originated from the USA and the UK, users in many other countries sent Tweets about antibiotics.

The vast majority of these Tweets were original messages, rather than reposts of Tweets from other people, which required users to engage with the content of the message. The daily global discussion about antibiotics did not remain homogeneous in content or volume during the analysis period and we found clearly defined peaks in activity relating to antibiotics.

Two of these peaks followed activities by the UK CMO concerning the threat of antimicrobial resistance, which stimulated discussions that reached a large global audience. It has not been possible to tell from our analysis what led to this particular impact compared with other announcements or events. In all four activity peaks, we found that Tweet activity reached maximal activity within 24 h and returned to the basal levels prior to the peak within 48 h. Thus, although messages about antibiotics spread rapidly in response to events, they do not lead to continued discussions on the Twitter platform at present.

Interestingly, no other antibiotic resistance awareness campaigns resulted in peaks of activity comparable to those seen following the four news announcements discussed above. This would suggest that until now such campaigns have not had a significant impact on this social media platform, which may be due to a lack of engagement with the most influential organizations or individuals on Twitter. Identifying key opinion leaders and gaining their support are essential steps in any public health intervention and appear to be even more crucial when using a network-based communication platform, as people who are more closely connected and interact more often are likely to have greater influence.

As three of the four activity peaks were related to antibiotic resistance, there appears to be a general receptiveness to messages on this topic and future campaigns should take this into consideration. Further, campaigns could look at ways to harness this willingness to discuss antimicrobial resistance into a more sustained dialogue. There is some evidence, however, that different types of users generate distinctive discourses on social media and therefore appealing to each audience would need adequate tailoring of messages; organizations and stakeholders in breast cancer, for example, would focus their Twitter updates on fundraising, screening and diagnosis, whilst the public would be significantly more likely to emphasize communal activities and events.

As far as we are aware, this is the first study that attempts to characterize events and occurrences driving people to discuss antibiotics on a social media platform. Our study has several limitations.

Second, our study was limited to terms in the English language only. Third, Topsy uses algorithms to determine whether the message is original or a Retweet and we cannot access these for verification. Fourth, the geographical location of individual Tweets is defined by the location of the user at the time of sending the Tweet and does not necessarily represent their habitual location. Finally, as highlighted in the Introduction, some demographic groups are under-represented amongst Twitter users, so care is required when considering extending our conclusions to other social media platforms and to the general population.

In conclusion, people across the globe talk about antibiotics on social media and free tools can be used rapidly to gain initial insight into discussion topics and triggers for changes in the volume of conversations. There is a need to understand the contribution and impact of social media tools on public health campaigns and institutional organizations must consider social media within their communications strategy.

More evidence is still needed regarding the optimal mix of communication interventions, including the timing of message posting and the individuals best suited to post messages. The development of tools capable of deeper analysis across a broader range of social media platforms may aid assessment of the impact of antibiotic awareness campaigns and promote understanding of opportunities to influence individuals' thoughts about antibiotic resistance.

All authors contributed to the development of the concept for the paper and approved the final version. We thank Topsy www. National Center for Biotechnology Information , U.

J Antimicrob Chemother. Published online May Oliver J. Holmes 2. Alison H. Author information Article notes Copyright and License information Disclaimer. This article has been cited by other articles in PMC. Abstract Objectives Social media has reshaped individual and institutional communication. Methods A web-based tool www. Conclusions Institutional events can rapidly amplify antibiotic discussions on social media, but their short lifespan may hinder their public impact.

Keywords: antibacterial agents, Internet, Web 2. Introduction The development of social media and social networks has provided unprecedented communication opportunities between individuals, companies and organizations. What is Twitter? Open in a separate window. Representation of user interactions on Twitter and flow of Tweets through the network. Use of Twitter in healthcare and public health The vast quantity of data generated in real time by users of the service has increasingly been exploited for healthcare and public health analysis.

Methods A web-based tool Topsy, www. Transparency declarations None to declare. Author contributions O. Acknowledgements We thank Topsy www. References 1. Statistic Brain. Social Networking Statistics.

Keim ME, Noji E. Emergent use of social media: a new age of opportunity for disaster resilience. Am J Disaster Med. Assessing vaccination sentiments with online social media: implications for infectious disease dynamics and control. PLoS Comput Biol. Analysing mood patterns in the United Kingdom through Twitter content. Computing Research Repository.

Duggan M, Brenner J. The demographics of social media users— Fox S, Jones S. The social life of health information. News use across social media platforms.

Pew Research Journalism Project. Collier N.

Identifying Topic-based Opinion Leaders in Social Networks by Content and User Information

The purpose of this paper is to use the practical application of tools provided by social network theory for the detection of potential influencers from the point of view of marketing within online communities. It proposes a method to detect significant actors based on centrality metrics. A matrix is proposed for the classification of the individuals that integrate a social network based on the combination of eigenvector centrality and betweenness centrality. The model is tested on a Facebook fan page for a sporting event. NodeXL is used to extract and analyze information. Semantic analysis and agent-based simulation are used to test the model. The proposed model is effective in detecting actors with the potential to efficiently spread a message in relation to the rest of the community, which is achieved from their position within the network.

Identifying Leaders and Followers in Online Social Networks

Forum has long been the main way of communication, and more and more users publish their opinions by it. The most influential users or opinion leaders will contribute to the formation of information, especially the positive influential users who can guide public opinions and make positive influence. Positive Opinion Leader Group POLG represents a group of users, each of who expresses the similar content and same sentiment orientation with their followers to a great extent, who are regarded as the most influential men during the information dissemination process. However, most existing researches pay less attention to the implicit relationship, heterogeneous structure and positive influence. In this paper, we focus on modeling multi-themes user network of forum with explicit and implicit links for this purpose.

It is valuable for the real world to find the opinion leaders. Because different data sources usually have different characteristics, there does not exist a standard algorithm to find and detect the opinion leaders in different data sources. Every data source has its own structural characteristics, and also has its own detection algorithm to find the opinion leaders. Experimental results show the opinion leaders and theirs characteristics can be found among the comments from the Weibo social network of China, which is like Facebook or Twitter in USA. With further study, the definition of opinion leader expands.

Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. DOI: Shafiq and M. Ilyas and Alex X.

What makes people talk about antibiotics on social media? A retrospective analysis of Twitter use

Identifying Topic-based Opinion Leaders in Social Networks by Content and User Information

Секретов отныне больше не существовало. Чтобы еще больше усилить впечатление о своей некомпетентности, АНБ подвергло яростным нападкам программы компьютерного кодирования, утверждая, что они мешают правоохранительным службам ловить и предавать суду преступников. Участники движения за гражданские свободы торжествовали и настаивали на том, что АНБ ни при каких обстоятельствах не должно читать их почту. Программы компьютерного кодирования раскупались как горячие пирожки. Никто не сомневался, что АНБ проиграло сражение. Цель была достигнута.

Не знаю, почему Фонтейн прикидывается идиотом, но ТРАНСТЕКСТ в опасности. Там происходит что-то очень серьезное. - Мидж.  - Он постарался ее успокоить, входя вслед за ней в комнату заседаний к закрытому жалюзи окну.  - Пусть директор разбирается. Она посмотрела ему в .

Этот чертов компьютер бьется над чем-то уже восемнадцать часов. Конечно же, все дело в вирусе. Чатрукьян это чувствовал. У него не было сомнений относительно того, что произошло: Стратмор совершил ошибку, обойдя фильтры, и теперь пытался скрыть этот факт глупой версией о диагностике. Чатрукьян не был бы так раздражен, если бы ТРАНСТЕКСТ был его единственной заботой.

New Trends in Networked Control of Complex Dynamic Systems: Theories and Applications

То, что он увидел, больше напоминало вход в преисподнюю, а не в служебное помещение. Узкая лестница спускалась к платформе, за которой тоже виднелись ступеньки, и все это было окутано красным туманом. Грег Хейл, подойдя к стеклянной перегородке Третьего узла, смотрел, как Чатрукьян спускается по лестнице. С того места, где он стоял, казалось, что голова сотрудника лаборатории систем безопасности лишилась тела и осталась лежать на полу шифровалки. А потом медленно скрылась из виду в клубах пара.

И прижала ладонь к горлу. - В шифровалке вырубилось электричество. Фонтейн поднял глаза, явно удивленный этим сообщением. Мидж подтвердила свои слова коротким кивком. - У них нет света.

Росио задумалась. - Нет, больше. В этот момент кровать громко заскрипела: клиент Росио попытался переменить позу. Беккер повернулся к нему и заговорил на беглом немецком: - Noch etwas. Что-нибудь. Что помогло бы мне найти девушку, которая взяла кольцо. Повисло молчание.

The Finding and Dynamic Detection of Opinion Leaders in Social Network

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