Types Of Tweets Research Articles

The geography of Twitter topics in London

Social media data are increasingly perceived as alternative sources to public attitude surveys because of the volume of available data that are time-stamped and (sometimes) precisely located. Such data can be mined to provide planners, marketers and researchers with useful information about activities and opinions across time and space. However, in their raw form, textual data are still difficult to analyse coherently and Twitter streams pose particular interpretive challenges because they are restricted to just 140 characters. This paper explores the use of an unsupervised learning algorithm to classify geo-tagged Tweets from Inner London recorded during typical weekdays throughout 2013 into a small number of groups, following extensive text cleaning techniques. Our classification identifies 20 distinctive and interpretive topic groupings, which represent key types of Tweets, from describing activities or informal conversations between users, to the use of check-in applets. Our motivation is to use the classification to demonstrate how the nature of the content posted on Twitter varies according to the characteristics of places and users. Topics and attitudes expressed through Tweets are found to vary substantially across Inner London, and by time of day. Some observed variations in behaviour on Twitter can be attributed to the inferred demographic and socio-economic characteristics of users, but place and local activities can also exert a considerable influence. Overall, the classification was found to provide a valuable framework for investigating the content and coverage of Twitter usage across Inner London.

Predicting Retweeting Behavior on Breast Cancer Social Networks: Network and Content Characteristics

This study explored how social media, especially Twitter, serves as a viable place for communicating about cancer. Using a 2-step analytic method that combined social network analysis and computer-aided content analysis, this study investigated (a) how different types of network structures explain retweeting behavior and (b) which types of tweets are retweeted and why some messages generate more interaction among users. The analysis revealed that messages written by users who had a higher number of followers, a higher level of personal influence over the interaction, and closer relationships and similarities with other users were retweeted. In addition, a tweet with a higher level of positive emotion was more likely to be retweeted, whereas a tweet with a higher level of tentative words was less likely to be retweeted. These findings imply that Twitter can be an effective tool for the dissemination of health information. Theoretical and practical implications for psychosocial interventions for people with health concerns are discussed.

Did I Tweet That?: Local Sports Broadcasters Are Not Using Twitter How They Think They Are

The purpose of this study was to examine how local sports broadcasters are using Twitter and if they are utilizing the service in the manner that they think they are. A mixed-methods approach was used to gather the data. A content analysis of 19,649 tweets from 201 local sports broadcasters throughout the country revealed the content of the tweets that were sent over a two-week period. Additionally, a survey was conducted with the owners of the same accounts to ask them about their Twitter use. Results determined that the local sports broadcasters were most frequently live-tweeting a game when using the social network. The survey responses revealed that the local sports broadcasters did not have an accurate assessment of their own Twitter activity, as most incorrectly identified which type of tweet they send most frequently. This demonstrates that most broadcasters are not stopping to think about what types of tweets they are sending to their followers.

Analysing and evaluating the task of automatic tweet generation: Knowledge to business

In this paper a study concerning the evaluation and analysis of natural language tweets is presented. Based on our experience in text summarisation, we carry out a deep analysis on user's perception through the evaluation of tweets manual and automatically generated from news. Specifically, we consider two key issues of a tweet: its informativeness and its interestingness. Therefore, we analyse: (1) do users equally perceive manual and automatic tweets?; (2) what linguistic features a good tweet may have to be interesting, as well as informative? The main challenge of this proposal is the analysis of tweets to help companies in their positioning and reputation on the Web. Our results show that: (1) automatically informative and interesting natural language tweets can be generated as a result of summarisation approaches; and (2) we can characterise good and bad tweets based on specific linguistic features not present in other types of tweets.

Sistem Pendeteksi Pengirim Tweet dengan Metode Klasifikasi Naive Bayes

Until Januari 2015, social media users reached 29% of the world population. In Indonesia itself had 28% active users from total populasi of Indonesia. The usage of social media gives positives and negatives effect. The negatives effect are the increasing number of fraud by using SMS or social media, such as Twitter. Many people are deceived by the tweet messages sent from known user account when in fact the sender is other person. Because of that, there is a need to have a system to detect wheteher the tweet sender is the same person or not. Naive Bayes classifiers method is used to classify that. The data source is taken from tokens selected based on two models, the minimum n-time number of occurrences and the n-th highest number of occurrences. Each tweets also processed into six different types of tweets, such as formal tweet or lowercase tweet. The test uses tenfold cross-validation and measured by the value of accuracy, precision, recall, and F-score. The common result shows 82,145% level of accuracy. Second model to select the tokens shows consistency level of accuracy for each types of tweets. The fifth types of tweets also get the highest level of accuracy for both models to select the tokens.

Nature of Tweets in the 2015 Nigerian Presidential Elections

Social media is becoming increasingly important as a means for social engagement. In Nigeria, Twitter is employed to convey opinion and make commentary on matters ranging from football to politics. Tweets are also used to inform, advocate, recruit and even incite. Previous studies have shown that Twitter could be effective for political mobilization. However, there is dearth of research on how Twitter has been used as a purveyor of neutral and/or hate speech in the Nigerian context. This study examined the nature of tweets in the immediate aftermath of the 2015 presidential election in Nigeria. The authors employed content analysis of 250 purposively selected tweets from the #Igbo hashtag which trended between March 29 and 31, 2015. The tweets were then categorized into five explicit hate and one neutral tweet category respectively. Results revealed the dominance of three hate tweet types: derogatory, mocking and blaming. These findings were then discussed bearing in mind earlier theories on the functionality of tweets and voting patterns from an analysis of the election results.

The use of Twitter to promote e-participation: connecting government and people

Twitter, the fastest growing social media tool, with its millions of users and being freely available and accessible, has become suitable for public organisations to communicate with stakeholders. Saudi Arabia has the largest percentage of online users using Twitter in the world. This paper discusses how Saudi municipalities use Twitter to communicate with citizens, by analysing tweeting patterns, tweet types, tweet content, follower reactions, and other statistics, in around 12,000 tweets, which account for all tweets sent out from the ten main municipalities. Results show that municipalities send out nine different types of tweets, with project information and calls for action tweets causing the most user reactions. Although, these types are seldom used despite their importance in improving citizen participation. Other detailed statistics and correlations are discussed in the paper and recommendations on how to maximise benefits of Twitter as a successful e-participation tool are explained.

Activating a global sport sponsorship with social media: an analysis of TOP sponsors, Twitter, and the 2014 Olympic Games

This research explored the use of Twitter for sponsorship activation by the The Olympic Program (TOP) sponsors during the 2014 Sochi Olympic Winter Games. Using NCapture, a total of 7,519 tweets and retweets of all ten TOP sponsors around the Games were captured from the 1st of January (pre-Olympic) to the 3rd of April, 2014 (post-Olympic). The data were analysed with the aid of Leximancer analytics software. Results show that there is no regular pattern of Twitter use for activation that was identifiable amongst the TOP sponsors; all were found adopting different strategic approaches. Notably, the TOP sponsors differ in terms of the number and types of tweets they produced, retweets they broadcasted, hashtags they rerun, and the registered growth of their followers from pre-games to post-games. The results also show three possible sponsorship objectives communicated by TOP sponsors, namely promotion (of services, products, and companys image), customer appreciation, and athletes encouragement. Potential theoretical and practical implications will be discussed, including suggestions for future research. The work encourages continued investigation.

트위터 상의 이미지 이용에 관한 분석

Given the context that users are actively using social media with multimedia embedded information, the purpose of this study is to demonstrate how images are used within Twitter messages, especially in influential and favorited messages. In order to achieve the purpose of this study, the top 200 influential and favorited messages with images were selected out of 1,589 tweets related to “Boston bombing” in April 2013. The characteristics of the message, image use, and user are analyzed and compared. Two phases of the analysis were conducted on three data sets containing the top 200 influential messages, top 200 favorited messages, and general messages. In the first phase, coding schemes have been developed for conducting three categorical analyses: (1) categorization of tweets, (2) categorization of image use, and (3) categorization of users. The three data sets were then coded using the coding schemes. In the second phase, comparison analyses were conducted among influential, favorited, and general tweets in terms of tweet type, image use, and user. While messages expressing opinion were found to be most favorited, the messages that shared information were recognized as most influential to users. On the other hand, as only four image uses - information dissemination, illustration, emotive/persuasive, and information processing - were found in this data set, the primary image use is likely to be data-driven rather than object-driven. From the perspective of users, the user types such as government, celebrity, and photo-sharing sites were found to be favorited and influential. An improved understanding of how users’ image needs, in the context of social media, contribute to the body of knowledge of image needs. This study will also provide valuable insight into practical designs and implications of image retrieval systems or services.

The Impacts of Structural Difference and Temporality of Tweets on Retrieval Effectiveness

To explore the information seeking behaviors in microblogosphere, the microblog track at TREC 2011 introduced a real-time ad-hoc retrieval task that aims at ranking relevant tweets in reverse-chronological order. We study this problem via a two-phase approach: 1) retrieving tweets in an ad-hoc way; 2) utilizing the temporal information of tweets to enhance the retrieval effectiveness of tweets. Tweets can be categorized into two types. One type consists of short messages not containing any URL of a Web page. The other type has at least one URL of a Web page in addition to a short message. These two types of tweets have different structures. In the first phase, to address the structural difference of tweets, we propose a method to rank tweets using the divide-and-conquer strategy. Specifically, we first rank the two types of tweets separately. This produces two rankings, one for each type. Then we merge these two rankings of tweets into one ranking. In the second phase, we first categorize queries into several types by exploring the temporal distributions of their top-retrieved tweets from the first phase; then we calculate the time-related relevance scores of tweets according to the classified types of queries; finally we combine the time scores with the IR scores from the first phase to produce a ranking of tweets. Experimental results achieved by using the TREC 2011 and TREC 2012 queries over the TREC Tweets2011 collection show that: (i) our way of ranking the two types of tweets separately and then merging them together yields better retrieval effectiveness than ranking them simultaneously; (ii) our way of incorporating temporal information into the retrieval process yields further improvements, and (iii) our method compares favorably with state-of-the-art methods in retrieval effectiveness.

The Complex Relationship of Realspace Events and Messages in Cyberspace: Case Study of Influenza and Pertussis Using Tweets

BackgroundSurveillance plays a vital role in disease detection, but traditional methods of collecting patient data, reporting to health officials, and compiling reports are costly and time consuming. In recent years, syndromic surveillance tools have expanded and researchers are able to exploit the vast amount of data available in real time on the Internet at minimal cost. Many data sources for infoveillance exist, but this study focuses on status updates (tweets) from the Twitter microblogging website.ObjectiveThe aim of this study was to explore the interaction between cyberspace message activity, measured by keyword-specific tweets, and real world occurrences of influenza and pertussis. Tweets were aggregated by week and compared to weekly influenza-like illness (ILI) and weekly pertussis incidence. The potential effect of tweet type was analyzed by categorizing tweets into 4 categories: nonretweets, retweets, tweets with a URL Web address, and tweets without a URL Web address.MethodsTweets were collected within a 17-mile radius of 11 US cities chosen on the basis of population size and the availability of disease data. Influenza analysis involved all 11 cities. Pertussis analysis was based on the 2 cities nearest to the Washington State pertussis outbreak (Seattle, WA and Portland, OR). Tweet collection resulted in 161,821 flu, 6174 influenza, 160 pertussis, and 1167 whooping cough tweets. The correlation coefficients between tweets or subgroups of tweets and disease occurrence were calculated and trends were presented graphically.ResultsCorrelations between weekly aggregated tweets and disease occurrence varied greatly, but were relatively strong in some areas. In general, correlation coefficients were stronger in the flu analysis compared to the pertussis analysis. Within each analysis, flu tweets were more strongly correlated with ILI rates than influenza tweets, and whooping cough tweets correlated more strongly with pertussis incidence than pertussis tweets. Nonretweets correlated more with disease occurrence than retweets, and tweets without a URL Web address correlated better with actual incidence than those with a URL Web address primarily for the flu tweets.ConclusionsThis study demonstrates that not only does keyword choice play an important role in how well tweets correlate with disease occurrence, but that the subgroup of tweets used for analysis is also important. This exploratory work shows potential in the use of tweets for infoveillance, but continued efforts are needed to further refine research methods in this field.