scholarly journals Clustering User Characteristics Based on the influence of Hashtags on the Instagram Platform

2019 ◽  
Vol 13 (4) ◽  
pp. 399
Author(s):  
Muhammad Habibi ◽  
Puji Winar Cahyo
Keyword(s):  
Social Media ◽  
User Characteristics ◽  
Clustering Method ◽  
Inaccurate Information ◽  
Total Usage

Instagram is a social media that has the potential to be used to increase awareness of a product. Approximately 70% of users spend their time searching for a product on Instagram. Many people promote their products with a lack of attention to the target. So that not infrequently the information distributed is inaccurate information and not following user characteristics. This study aims to cluster the characteristics of Instagram users based on hashtag compatibility. The method used in this study is the K-Means Clustering method. Based on the results of the experiment, this research succeeded in clustering Instagram users based on the hashtag match on the text caption. Besides, TF-IDF can be used as a feature suitable for the K-Means Klastering method. The results of the hashtag "#kopi" analysis resulted in hashtag suggestions that can be used for the promotion of a product related to coffee, including the hashtag #coffeeshop and #coffee with total usage of 14968 captions.

scholarly journals Social Media Vaccine Websites: A Comparative Analysis of Public and Moderated Websites

2018 ◽  
Vol 46 (3) ◽  
pp. 454-462 ◽  
Author(s):  
Jo Ann Shoup ◽  
Komal J. Narwaney ◽  
Nicole M. Wagner ◽  
Courtney R. Kraus ◽  
Kathy S. Gleason ◽  
...  
Keyword(s):  
Social Media ◽  
The Internet ◽  
Discussion Boards ◽  
Discussion Forums ◽  
Chi Square ◽  
Inaccurate Information ◽  
Search Terms ◽  
Quantitative Results ◽  
Vaccine Information ◽  
Observation Period

The internet is an important source of vaccine information for parents. We evaluated and compared the interactive content on an expert moderated vaccine social media (VSM) website developed for parents of children 24 months of age or younger and enrolled in a health care system to a random sample of interactions extracted from publicly available parenting and vaccine-focused blogs and discussion forums. The study observation period was September 2013 through July 2016. Three hundred sixty-seven eligible websites were located using search terms related to vaccines. Seventy-nine samples of interactions about vaccines on public blogs and discussion boards and 61 interactions from the expert moderated VSM website were coded for tone, vaccine stance, and accuracy of information. If information was inaccurate, it was coded as corrected, partially corrected or uncorrected. Using chi-square or Fisher’s exact tests, we compared coded interactions from the VSM website with coded interactions from the sample of publicly available websites. We then identified representative quotes to illustrate the quantitative results. Tone, vaccine stance, and accuracy of information were significantly different (all p < .05). Publicly available vaccine websites tended to be more contentious and have a negative stance toward vaccines. These websites also had inaccurate and uncorrected information. In contrast, the expert moderated website had a more civil tone, minimal posting of inaccurate information, with very little participant-to-participant interaction. An expert moderated, interactive vaccine website appears to provide a platform for parents to gather accurate vaccine information, express their vaccine concerns and ask questions of vaccine experts.

Detection of Leading Experts from ResearchGate

2018 ◽  
Vol 5 (3) ◽  
pp. 67-86
Author(s):  
Eya Ben Ahmed
Keyword(s):  
Social Media ◽  
Social Network ◽  
Technological Development ◽  
Clustering Method ◽  
Web Based ◽  
Supervised Clustering ◽  
And Performance

This article describes how thanks to the technological development, social media has propagated in recent years. The latter describes a range of Web-based platforms that enable people to socially interact with one another online. Several types of social media appeared. In this context, the author focuses on scientific social network which connects the researchers and allow them to communicate and collaborate online. In this paper, we, particularly, aim to detect the scientific leaders through firstly detect communities in social network then identify the leader of each group. To do this, the author introduces a new hierarchical semi-supervised clustering method based on ordinal density. The results of carried out experiments on real scientific warehouse have shown significant profits in terms of accuracy and performance.

Measuring Twitter Use: Validating Survey-Based Measures

2019 ◽  
pp. 089443931989624 ◽  
Author(s):  
Michael Henderson ◽  
Ke Jiang ◽  
Martin Johnson ◽  
Lance Porter
Keyword(s):  
Social Media ◽  
Media Use ◽  
User Characteristics ◽  
Social Media Use ◽  
Individual Level ◽  
Self Reports ◽  
Important Challenge ◽  
Twitter Activity ◽  
Survey Responses ◽  
Observed Behavior

An important challenge for research on social media use is to relate users’ activity on these platforms to user characteristics such as demographics. Surveys allow researchers to measure these characteristics but may be subject to measurement error in self-reported social media use. We compare survey responses to observed behavior in order to assess the validity of self-reported frequency of posting to Twitter, retweeting content, sharing photos, sharing videos, and sending direct messages. Additionally, we examine correlations between self-reported and observed behavior across a range of time frames, from 1 month to 114 months before the survey. We find variation in the quality of self-reports across types of Twitter activity. We also find that self-reports about posting and retweeting tend to reflect recent activity, while self-reports about other activities tend to reflect behavior over a longer span. Furthermore, we find that two characteristics of experience with the platform—the length of time that a person has been active on Twitter and how much their activity on the platform changes over time—predict individual-level discrepancies between survey response and observed behavior, but these discrepancies cancel out when averaged across individuals. Nevertheless, other sources of bias remain. Taken together, our results indicate that while surveys are quite useful for collecting characteristics of social media users, relying on self-reported social media behavior distorts inferential results from what is found when relying on observed social media behavior.

scholarly journals 140 Characters of Intoxication: Exploring the Prevalence of Alcohol-Related Tweets and Predicting Their Virality

SAGE Open ◽  
2018 ◽  
Vol 8 (4) ◽  
pp. 215824401880313 ◽  
Author(s):  
Saleem Alhabash ◽  
Courtland VanDam ◽  
Pang-Ning Tan ◽  
Sandi W. Smith ◽  
Gregory Viken ◽  
...  
Keyword(s):  
Social Media ◽  
Alcohol Consumption ◽  
Media Use ◽  
Past Research ◽  
The United States ◽  
Excessive Drinking ◽  
User Characteristics ◽  
Social Media Use ◽  
Media Platform ◽  
Widespread Phenomenon

Past research has suggested an associative relationship between social media use and alcohol consumption, especially among the younger generations. The current study takes a generalizable approach to examining the prevalence of posting about alcohol on a popular social media platform, Twitter, as well as examining the predictors of a tweet’s virality. We content-analyzed more than 47.5 million tweets that were posted in March 2015 to explore the prevalence of alcohol-related references, and how alcohol-related references, tweet features (e.g., inclusion of hashtags, pictures, etc.), and user characteristics (e.g., number of followers) contribute to the tweet’s virality. Our findings showed that during March 2015, about two of every 100 tweets in the United States were alcohol-related; whereas the majority of those referenced intoxication. In addition to tweet features and user characteristics, the prevalence of alcohol-related references in a tweet positively predicted the number of likes it received, yet negatively predicted the number of retweets. Given prior evidence supporting the association between social media use and alcohol consumption, the prevalence of alcohol references in tweets and how that contributes to their virality offers insights into the widespread phenomenon of glorifying alcohol use and excessive drinking via social media, pointing to potential negative health consequences.

scholarly journals Effective Communication in Social Media

2019 ◽  
pp. 25
Author(s):  
Suhazeli Abdullah
Keyword(s):  
Social Media ◽  
Medical Information ◽  
Public Image ◽  
Health Issues ◽  
Health Seeking ◽  
Medical Provider ◽  
Great Opportunity ◽  
Misleading Information ◽  
Inaccurate Information ◽  
Medical Issues

I’ve never considered myself a religious man, but lately I’ve found myself preaching one key message to the community repeatedly – the need to recognize the importance of harnessing social media as part of their ongoing communication and health seeking behaviour. Gone are the days of press kits and faxing press releases. Social Media has become a game changer. Don’t believe me? Facebook alone has over 1.2 billion monthly active users, Twitter over one billion registered users and Instagram over 300 million. Nonetheless, there is strong evidence to suggest that at least part of the source of this trend is the degree to which medically inaccurate information about medical issues surface on the websites where many netizens get their information. Social media survey from MCMC (2017) stated that 77.2% of netizen search medical information including virtual consultation via internet. Sadly, 82.7% of them said, the information that they gathered from internet were trusted. Since most netizens would probably read the first few pages of the internet search results, study shown by American Academy of Physician, only 43.5% of sampled websites contained recommendations that were in line with the AAP recommendations, while 28.1% contained inaccurate information and 28.4% of the websites were not medically relevant. Hence the virtual medical information are not designed to distinguish quality information from misinformation or misleading information, and the consequences of that are particularly troubling for public health issues. Medical provider must put forward an effort to curb this myth and misleading information through social media. Social media plays a crucial role in connecting people and developing relationships, not only with key influencers and journalists covering medical fraternity, but also provides a great opportunity to establish patient's service by gathering input, answering questions and listening to their comment. The insight you gain from social media listening provide our professiona lism with a better understanding of what’s working and what’s not, and goes a long way in helpin g your public image. It’s important to be aware real-time of what people are saying about our fraternity.International Journal of Human and Health Sciences Supplementary Issue: 2019 Page: 25

scholarly journals Developing an accuracy-prompt toolkit to reduce COVID-19 misinformation online

2021 ◽  
Author(s):  
Ziv Epstein ◽  
adam berinsky ◽  
Rocky Cole ◽  
Andrew Gully ◽  
Gordon Pennycook ◽  
...  
Keyword(s):  
Social Media ◽  
Geographic Region ◽  
User Characteristics ◽  
Wide Range ◽  
The Difference ◽  
False News ◽  
Demographic Subgroups ◽  
Large Survey ◽  
College Educated

Recent research suggests that shifting users’ attention to accuracy increases the quality of news they subsequently share online. Here we help develop this initial observation into a suite of deployable interventions for practitioners. We ask (i) how prior results generalize to other approaches for prompting users to consider accuracy, and (ii) for whom these prompts are more versus less effective. In a large survey experiment examining participants’ intentions to share true and false headlines about COVID-19, we identify a variety of different accuracy prompts that successfully increase sharing discernment across a wide range of demographic subgroups while maintaining user autonomy. Research questions•There is mounting evidence that inattention to accuracy plays an important role in the spread of misinformation online. Here we examine the utility of a suite of different accuracy prompts aimed at increasing the quality of news shared by social media users.•Which approaches to shifting attention towards accuracy are most effective? •Does the effectiveness of the accuracy prompts vary based on social media user characteristics? Assessing effectiveness across subgroups is practically important for examining the generalizability of the treatments, and is theoretically important for exploring the underlying mechanism.Essay summary•Using survey experiments with N=9,070 American social media users (quota-matched to the national distribution on age, gender, ethnicity, and geographic region), we compared the effect of different treatments designed to induce people to think about accuracy when deciding what news to share. Participants received one of the treatments (or were assigned to a control condition), and then indicated how likely they would be to share a series of true and false news posts about COVID-19. •We identified three lightweight, easily-implementable approaches that each increased sharing discernment (the quality of news shared, measured as the difference in sharing probability of true versus false headlines) by roughly 50%, and a slightly more lengthy approach that increased sharing discernment by close to 100%. We also found that another approach that seemed promising ex ante (descriptive norms) was ineffective. Further-more, gender, race, partisanship, and concern about COVID-19 did not moderate effectiveness, suggesting that the accuracy prompts will be effective for a wide range of demographic subgroups. Finally, helping to illuminate the mechanism behind the effect, the prompts were more effective for participants who were more attentive, reflective, engaged with COVID-related news, concerned about accuracy, college-educated, and middle-aged. •From a practical perspective, our results suggest a menu of accuracy prompts that are effective in our experimental setting and that technology companies could consider testing on their own services.

The dashang feature in social media: a personality and justice theory perspective

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jinlin Wan ◽  
Yaobin Lu ◽  
Sumeet Gupta
Keyword(s):  
Social Media ◽  
Big Five ◽  
Big Five Personality ◽  
Justice Theory ◽  
User Characteristics ◽  
Interpersonal Justice ◽  
Content Type ◽  
Informational Justice ◽  
Social Media Platforms

PurposeDashang refers to a reward given voluntarily to street performers in return for their performance. Some social media platforms have created a way to integrate this as a function, referred to as the dashang feature, to allow users to reward live performers online as well. Over the last few years, this function has become extremely popular among social media users, as it recreates the nostalgic experience of watching street performances. Platforms now consider it indispensable, as it has become a source of substantial revenue (commission on rewards earned by performers). However, not all users reward performers. For each user who pays, there are many more who lurk on the platform. This study examines the reasons for these differences using the Big Five personality perspective and justice theory.Design/methodology/approachWe develop an empirical model using the Big Five theory and justice theory and test it using empirical data collected through a survey of WeChat users.FindingsThe results indicate that distributive justice, interpersonal justice and informational justice are essential factors in relation to social media users' use of the dashang feature. It is also found that personality type affects these three factors.Originality/valueThis study makes three key contributions. First, it examines the factors that influence users' voluntary use of the dashang feature using the lenses of the Big Five theory and justice theory. Second, this study extends previous results on perceived justice to examine use of the dashang feature in social media. Third, this study applies these theories to the study of consumer behavior by exploring the role of user characteristics in social media use.

A Deep Learning Based Approach for Fake News Detection

2021 ◽  
pp. 388-394
Author(s):  
Srishti Sharma ◽  
Vaishali Kalra
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Language Processing ◽  
Research Effort ◽  
Political Polarization ◽  
Fake News ◽  
User Characteristics ◽  
Mainstream Media ◽  
Boosting Method ◽  
Partisan Conflict

Owing to the rapid explosion of social media platforms in the past decade, we spread and consume information via the internet at an expeditious rate. It has caused an alarming proliferation of fake news on social networks. The global nature of social networks has facilitated international blowout of fake news. Fake news has proven to increase political polarization and partisan conflict. Fake news is also found to be more rampant on social media than mainstream media. The evil of fake news is garnering a lot of attention and research effort. In this work, we have tried to handle the spread of fake news via tweets. We have performed fake news classification by employing user characteristics as well as tweet text. Thus, trying to provide a holistic solution for fake news detection. For classifying user characteristics, we have used the XGBoost algorithm which is an ensemble of decision trees utilising the boosting method. Further to correctly classify the tweet text we used various natural language processing techniques to preprocess the tweets and then applied a sequential neural network and state-of-the-art BERT transformer to classify the tweets. The models have then been evaluated and compared with various baseline models to show that our approach effectively tackles this problemOwing to the rapid explosion of social media platforms in the past decade, we spread and consume information via the internet at an expeditious rate. It has caused an alarming proliferation of fake news on social networks. The global nature of social networks has facilitated international blowout of fake news. Fake news has proven to increase political polarization and partisan conflict. Fake news is also found to be more rampant on social media than mainstream media. The evil of fake news is garnering a lot of attention and research effort. In this work, we have tried to handle the spread of fake news via tweets. We have performed fake news classification by employing user characteristics as well as tweet text. Thus, trying to provide a holistic solution for fake news detection. For classifying user characteristics, we have used the XGBoost algorithm which is an ensemble of decision trees utilising the boosting method. Further to correctly classify the tweet text we used various natural language processing techniques to preprocess the tweets and then applied a sequential neural network and state-of-the-art BERT transformer to classify the tweets. The models have then been evaluated and compared with various baseline models to show that our approach effectively tackles this problem

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