98,536 research outputs found
Audience Gatekeeping in the Twitter Service: An Investigation of Tweets about the 2009 Gaza Conflict
Twitter is a social news service in which information is selected and distributed by individual members of the tweet audience. While communication literature has studied traditional news media and the propagation of information, to our knowledge there have been no studies of the new social media and their impacts on the propagation of news during extreme event situations. This exploration attempts to build an understanding of how preexisting hyperlink structures on the Web and different types of information channels affect Twitter audiences’ information selection. The study analyzes the concentration of user-selected information sources in Twitter about the 2009 Israel-Gaza conflict. There are three findings. First, a statistical test of a power-law structure revealed that, while a wide range of information was selected and redistributed by Twitter users, the aggregation of these selections over-represented a small number of prominent websites. Second, binomial regression analyses showed that Twitter user selections were not constituted randomly but were affected by the number of hyperlinks received and the types of information channels. Third, temporal analyses revealed that sources via social media channels were more prominently selected especially in the later stages of the news information lifespan
Western solidarity with Pussy Riot and the Twittering of cosmopolitan selves
This article aims to explain the widespread attention to contemporary protesting artists among Western audiences by focusing on the case of Pussy Riot. Social movement scholarship provides a first step into understanding how Pussy Riot legitimately protests Russian politics through its punk performances. It then turns to the concept of cosmopolitanism as a performance in everyday life to explain Pussy Riot's appeal among Western audiences. By collecting and analyzing 9001 tweets through a thematic hashtag analysis and topic modeling, this article analyzes how audiences talk about Pussy Riot and shows how Twitter affords users to perform cosmopolitan selves by sharing their ideas and experiences on Pussy Riot with others. Although we distinguish between four types of cosmopolitan selves, the results clearly show Pussy Riot is mainly reflected upon in a media context: Twitter users predominantly talk about Pussy Riot's media appearances rather than readily engage with its explicit political advocacy
A Comparison of Translation Shifts in Jokowi’s Instagram and Twitter Accounts Using Machine Translation
Translation shifts occur when content is translated from one language to another, due to either target language structure constraints (servitude) or translators' choices (options). With the growing use of machine translation services like Instagram Translate and Twitter Auto Translate on social media, understanding these shifts is important for effective cross-cultural communication. This study aims to compare translation shifts in the English translations of Jokowi's Instagram and Twitter accounts, focusing in the performance of these machine translation services. The research will analyze a sample of posts from Jokowi's Instagram and Twitter accounts, translated by Instagram Translate and Twitter Auto Translate. The study will use a combination of quantitative and qualitative analysis to identify different types of shifts, such as level shifts and category shifts, and to determine whether they result from servitude or optional choices made by the translation algorithms. Additionally, the research will assess the accuracy and idiomatic quality of the translations produced by these services, considering potential challenges related to context, idiomatic expressions, and cultural references. The findings will help improve understanding of machine translation performance in social media contexts and inform future improvements to translation algorithms. By examining translation shifts in Jokowi's Instagram and Twitter accounts, this study seeks to enhance communication and understanding on social media platforms across language barriers. The research also aims to provide valuable insights for users of machine translation services, particularly within the rapidly changing domain of social media platforms.
Personal activity centres and geosocial data analysis: Combining big data with small data
Understanding how people move and interact within urban settings has been greatly facilitated by the expansion of personal computing and mobile studies. Geosocial data derived from social media applications have the potential to both document how large segments of urban populations move about and use space, as well as how they interact with their environments. In this paper we examine spatial and temporal clustering of individuals’ geosocial messages as a way to derive personal activity centres for a subset of Twitter users in the City of Toronto. We compare the two types of clustering, and for a subset of users, compare to actual self-reported activity centres. Our analysis reveals that home locations were detected within 500 m for up to 53% of users using simple spatial clustering methods based on a sample of 16 users. Work locations were detected within 500 m for 33% of users. Additionally, we find that the broader pattern of geosocial footprints indicated that 35% of users have only one activity centre, 30% have two activity centres, and 14% have three activity centres. Tweets about environment were more likely sent from locations other than work and home, and when not directed to another user. These findings indicate activity centres defined from Twitter do relate to general spatial activities, but the limited degree of spatial variability on an individual level limits the applications of geosocial footprints for more detailed analyses of movement patterns in the city
How do we Tweet? The Comparative Analysis of Twitter Usage by Message Types, Devices, and Sources
Facing the growing importance of social media in the marketing field, this study is intended to build a better understanding of Twitter usage. A total of 73,192 tweets were examined by message types, devices and platforms used. Instead of relying on the audience’s response (e.g., survey or experiment) or traditional content analysis, this study used a data-mining approach and software that are widely used in the computer science field. Overall findings indicate that individual users prefer mobile devices to desktops and use more official web pages or mobile applications provided by Twitter when they tweet, and their most popular message type was the Singleton, an undirected message with no specific recipient. However, we also found that tweets generated through business sources were different from those through official sources in terms of message type, devices, and the nature. The implications of these findings were discussed
Analyzing emotions on Twitter during the 2014 Purdue University shooting crisis
Social media has recently attracted much attention as an emergency management tool. Providing public emotional support is one of the aspects of emergency management where social media can be extremely useful. However, before we can effectively use social media for this purpose, we need to fully understand the dynamics of emotions on social media platforms. This study contributes to this understanding. In this thesis I look into the emotional expressions on Twitter following the 2014 Purdue shooting incident. I analyze how different types of users emotionally reacted to the incident during the critical one and a half hours following the shooting. Various types of emotions and their trends over this time period are described and analyzed. Moreover, I give some suggestions for providing effective emotional support using social media in times of crisis
TwitterMancer: Predicting Interactions on Twitter Accurately
This paper investigates the interplay between different types of user
interactions on Twitter, with respect to predicting missing or unseen
interactions. For example, given a set of retweet interactions between Twitter
users, how accurately can we predict reply interactions? Is it more difficult
to predict retweet or quote interactions between a pair of accounts? Also, how
important is time locality, and which features of interaction patterns are most
important to enable accurate prediction of specific Twitter interactions? Our
empirical study of Twitter interactions contributes initial answers to these
questions.
We have crawled an extensive dataset of Greek-speaking Twitter accounts and
their follow, quote, retweet, reply interactions over a period of a month.
We find we can accurately predict many interactions of Twitter users.
Interestingly, the most predictive features vary with the user profiles, and
are not the same across all users.
For example, for a pair of users that interact with a large number of other
Twitter users, we find that certain "higher-dimensional" triads, i.e., triads
that involve multiple types of interactions, are very informative, whereas for
less active Twitter users, certain in-degrees and out-degrees play a major
role. Finally, we provide various other insights on Twitter user behavior.
Our code and data are available at https://github.com/twittermancer/.
Keywords: Graph mining, machine learning, social media, social network
Organizational Twitter Use: A Qualitative Analysis of Tweets During Breast Cancer Awareness Month
One in eight women will develop breast cancer in her lifetime. The best-known awareness event to fight the health issue is Breast Cancer Awareness Month (BCAM). Twitter is a growing source of health information amongst users; however, little research exists into understanding how various organizations use their Twitter accounts to communicate about breast cancer during BCAM, as well as implications of this use for the health information consumers. In this context, there is also a dearth of research about if, and how organizations use behavioral change theories to tailor their social media content or not. The paper explored through qualitative content analysis how four different health related organizations- Susan G. Komen, US News Health, Woman’s Hospital and Breast Cancer Social Media use their Twitter accounts to talk about breast cancer during the Breast Cancer Awareness Month (BCAM). In this study, all the tweets by these organizations were analyzed through the framework of behavioral change theory- Health Belief Model (HBM). The main purpose of this research study was to examine the tweets of the varied organizations for the presence or absence of theoretical constructs of Health Belief Model such as perceived threat, perceived benefits, perceived barriers and cues to action, which inform about the potential for users to take protective action against breast cancer. A content analysis based on theoretical lens of Health Belief Model (HBM) of 2916 tweets revealed that majority of the tweets posted by these organizations did not reflect the theoretical constructs of Health Belief Model. Out of all the tweets that represented the theoretical constructs, it was observed that “perceived barrier” (n= 781, 26.37%), was in the maximum number. This was followed by “cues to action” (n= 711, 24.01%), “perceived benefits” (n=397, 13.40%) and “perceived threat” (n=230, 7.76%). Overall the study demonstrated that different organizations shared valuable breast cancer related content on Twitter and each Twitter outlet took a different approach to its use of Twitter, evident through focus on different types of breast cancer related content, use of elements like hashtags and videos etc
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