Monitoring Twitter strategies to discover resonating topics: The case of the UNDP (original) (raw)
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Analysis of Twitter and YouTube during USelections 2020
arXiv (Cornell University), 2020
The presidential elections in the United States on 3 November 2020 have caused extensive discussions on social media. A part of the content on US elections is organic, coming from users discussing their opinions of the candidates, political positions, or relevant content presented on television. Another significant part of the content generated originates from organized campaigns, both official and by astroturfing. In this study, we obtain approximately 17.5M tweets containing 3M users, based on prevalent hashtags related to US election 2020, as well as the related YouTube links, contained in the Twitter dataset, likes, dislikes and comments of the videos and conduct volume, sentiment and graph analysis on the communities formed. Particularly, we study the daily traffic per prevalent hashtags, plot the retweet graph from July to September 2020, show how its main connected component becomes denser in the period closer to the elections and highlight the two main entities ('Biden' and 'Trump'). Additionally, we gather the related YouTube links contained in the previous dataset and perform sentiment analysis. The results on sentiment analysis on the Twitter corpus and the YouTube metadata gathered, show the positive and negative sentiment for the two entities throughout this period. The results of sentiment analysis indicate that 45.7% express positive sentiment towards Trump in Twitter and 33.8% positive sentiment towards Biden, while 14.55% of users express positive sentiment in YouTube metadata gathered towards Trump and 8.7% positive sentiment towards Biden. Our analysis fill the gap between the connection of offline events and their consequences in social media by monitoring important events in real world and measuring public volume and sentiment before and after the event in social media. CCS CONCEPTS • Networks → Online social networks; • Information systems → Sentiment analysis.
Computer Communications, 2016
Twitter is often referred to as a backchannel for conferences. While the main conference takes place in a physical setting, on-site and off-site attendees socialize, introduce new ideas or broadcast information by microblogging on Twitter. In this paper we analyze scholars' Twitter usage in 16 Computer Science conferences over a timespan of five years. Our primary finding is that over the years there are differences with respect to the uses of Twitter, with an increase of informational activity (retweets and URLs), and a decrease of conversational usage (replies and mentions), which also impacts the network structure-meaning the amount of connected components-of the informational and conversational networks. We also applied topic modeling over the tweets' content and found that when clustering conferences according to their topics the resulting dendrogram clearly reveals the similarities and differences of the actual research interests of those events. Furthermore, we also analyzed the sentiment of tweets and found persistent differences among conferences. It also shows that some communities consistently express messages with higher levels of emotions while others do it in a more neutral manner. Finally, we investigated some features that can help predict future user participation in the online Twitter conference activity. By casting the problem as a classification task, we created a model that identifies factors that contribute to the continuing user participation. Our results have implications for research communities to implement strategies for continuous and active participation among members. Moreover, our work reveals the potential for the use of information shared on Twitter in order to facilitate communication and cooperation among research communities, by providing visibility to new resources or researchers from relevant but often little known research communities.
Information resonance on Twitter
Proceedings of the First Workshop on Social Media Analytics - SOMA '10, 2010
Twitter has undoubtedly caught the attention of both the general public, and academia as a microblogging service worthy of study and attention. Twitter has several features that sets it apart from other social media/networking sites, including its 140 character limit on each user's message (tweet), and the unique combination of avenues via which information is shared: directed social network of friends and followers, where messages posted by a user is broadcast to all its followers, and the public timeline, which provides real time access to posts or tweets on specific topics for everyone. While the character limit plays a role in shaping the type of messages that are posted and shared, the dual mode of sharing information (public vs posts to one's followers) provides multiple pathways in which a posting can propagate through the user landscape via forwarding or "Retweets", leading us to ask the following questions: How does a message resonate and spread widely among the users on Twitter, and are the resulting cascade dynamics different due to the unique features of Twitter? What role does content of a message play in its popularity? Realizing that tweet content would play a major role in the information propagation dynamics (as borne out by the empirical results reported in this paper), we focused on patterns of information propagation on Twitter by observing the sharing and reposting of messages around a specific topic, i.e. the Iranian election. We know that during the 2009 post-election protests in Iran, Twitter and its large community of users played an important role in disseminating news, images, and videos worldwide and in documenting the events. We collected tweets of more than 20 million publicly accessible users on Twitter and analyzed over three million tweets related to the Iranian election posted by around 500K users during June and July of 2009. Our results provide several key in-* The work was entirely performed when J. Kong was at UCLA.
SOCIAL TRENDS: THE THEORY, RESEARCH AND SOCIALITY DISCERNED THROUGH TWITTER
Communication is essential in every human activity it enables a person to express one's thought. There are different ways on how to convey the message to another person or a group, still interpretation of this messages are misinterpreted and worst the receiver of the message cannot make sense of the message at all. In the advent of technology, different tools and platform started to emerge to make communication better and faster. This paper discusses the theory, research and sociality discerned through Twitter. The data set used were retrieved from twitter between the midnight of Monday, February 27, 2017 and the morning of February 28, 2017. The extracted tweet were analyzed, identifying the top trending topic of the day #Oscars, analyzed the characters within and discovered tweet and tweet entities utilizing Frequency Analysis. The lexical diversity of the tweet was computed and histogram was used to visualize the frequency of data. Future directions for social trends includes the following: testing on how twitter conversation and topics affects a specific community or decision on controversial issues; implement an additional approach; and utilizing the sociality discerned through Twitter in predicting behavior of a person or a group of people and in other field that resolve human activities and interaction.
Handbook Twitter for Research, 2015 / 2016
EMLYON Press, 2016
Close to 2,500 research papers on Twitter have been published since 2006, the year the social networking service was founded (see Fausto & Aventurier, chapter 1 in this volume). Scientists have developed an interest in Twitter as an object of study itself, and also as a rich and convenient data source to conduct research on topics independent of Twitter-from the prediction of election results to new challenges in sentiment analysis. The volume and diversity of these scientific contributions would suggest that fruitful exchanges would take place between researchers sharing an interest in Twitter. Yet, science being structured in separate fields, a consequence is that one researcher using Twitter data in her scientific domain might never cross the path of another scientist using the same type of data but in a different scientific domain. Specialists of business-to-business marketing rarely interact with students in literary studies, while sociologists of urban life seldom engage with the forefront of text mining research-even if they all use Twitter as an input to explore their respective research question. It is reasonable to think that scholars situated in vastly different research traditions would benefit from meeting and exchanging views on how they use Twitter, for which purpose and for what results. The conference « Twitter for Research » was organized on April 24, 2015 in Lyon, France with the ambition to fulfill this promise: gathering scientists using Twitter in their research, from all disciplinary backgrounds, for a day of exchange, communication and networking. The present volume gathers 13 contributions which were presented this day (see the full program of the conference at http://tinyurl.com/twitter4research2015). This Handbook is the first edition (2015/2016) of an annual series devoted to cover research contributions harnessing Twitter as a key input. To be considered for the next edition, we encourage you to submit a proposal for the next conference "Twitter for Research", due to be organized in Galway, Dublin on April 18-20, 2016.
What people study when they study Twitter: Classifying Twitter related academic papers
Journcal of Documentation, 2013
Purpose: Since its introduction in 2006, messages posted to the microblogging system Twitter have provided a rich dataset for researchers, leading to the publication of over a thousand academic papers. This paper aims to identify this published work and to classify it in order to understand Twitter based research. Design/methodology/approach: Firstly the papers on Twitter were identified. Secondly, following a review of the literature, a classification of the dimensions of microblogging research was established. Thirdly, papers were qualitatively classified using open coded content analysis, based on the paper’s title and abstract, in order to analyze method, subject, and approach. Findings: The majority of published work relating to Twitter concentrates on aspects of the messages sent and details of the users. A variety of methodological approaches are used across a range of identified domains. Research Limitations: This work reviewed the abstracts of all papers available via database search on the term “Twitter” and this has two major implications: 1) the full papers are not considered and so works may be misclassified if their abstract is not clear, 2) publications not indexed by the databases, such as book chapters, are not included. The study is focussed on microblogging, the applicability of the approach to other media is not considered. Originality/value: To date there has not been an overarching study to look at the methods and purpose of those using Twitter as a research subject. Our major contribution is to scope out papers published on Twitter until the close of 2011. The classification derived here will provide a framework within which researchers studying Twitter related topics will be able to position and ground their work. Keywords: Twitter, Microblogging, Abstracts, Papers, Classification, Social Network Systems. Paper type: Research paper
What do people study when they study Twitter? Classifying Twitter related academic papers
Journal of Documentation, 2013
PurposeSince its introduction in 2006, messages posted to the microblogging system Twitter have provided a rich dataset for researchers, leading to the publication of over a thousand academic papers. This paper aims to identify this published work and to classify it in order to understand Twitter based research.Design/methodology/approachFirstly the papers on Twitter were identified. Secondly, following a review of the literature, a classification of the dimensions of microblogging research was established. Thirdly, papers were qualitatively classified using open coded content analysis, based on the paper's title and abstract, in order to analyze method, subject, and approach.FindingsThe majority of published work relating to Twitter concentrates on aspects of the messages sent and details of the users. A variety of methodological approaches is used across a range of identified domains.Research limitations/implicationsThis work reviewed the abstracts of all papers available via ...