What is the Conversation About?

Sommer, Stefan; Schieber, Andreas; Heinrich, Kai; Hilbert, Andreas

doi:10.4018/jiit.2012010102

Cited by 14 publications

(7 citation statements)

References 29 publications

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“…Therefore, retweeted and favorited tweets were included in the analysis because they reflected the interest in the preventive behaviors. Because the raw data could not be directly analyzed, as suggested by Sommer et al (2012), the data were subjected to a two-step pre-process cleaning procedure before the analysis.…”

Section: Data Collection and Cleaningmentioning

confidence: 99%

Trends in Preventive Behaviors Across Countries: An Exploratory Study of English Language Tweets

Furukawa¹,

Kobayashi²

2022

Journal of Social Sciences

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People take preventive behaviors to keep themselves from harm and protect themselves from physical harm, psychological threats, or health issues. Previous studies have suggested that preventive behavior trends are closely connected to cultures and values. However, the specific preventive behavior categories, their specific elements, and the preventive behavior differences between countries have never been fully examined. Therefore, using a topic modeling approach to analyze tweets from across the world, this study categorized the preventive behaviors and identified the specific trends in 68 countries. Six main topic foci were found: Life, disease, saving others, physical, psychological, and crime; the extent of these preventive behaviors in each country was analyzed to determine how they were organized and valued in each country. The results of this study could be useful for international firm management, marketing, and government policies and be the starting point for further international preventive behavior studies.

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Section: Data Collection and Cleaningmentioning

confidence: 99%

Trends in Preventive Behaviors Across Countries: An Exploratory Study of English Language Tweets

Furukawa¹,

Kobayashi²

2022

Journal of Social Sciences

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“…Techniques such as topic modeling can also be used to infer relevant filters with an important extra-cost compared with our approach. In (Sommer et al, 2012), the authors describe a method which enables to label tweets with topics in order to provide sentiment analysis in an e-commerce context. We underline that our work aims at tackling scalability issues in such a context.…”

Section: Filtersmentioning

confidence: 99%

Filtering Structures for Microblogging Content

Dahimene

Mouza

2015

International Journal of Intelligent Information Technologies

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In the last years, microblogging systems have encountered a large success. After 7 years of existence Twitter claims more than 271 million active accounts leading to 500 million tweets per day. Microblogging systems rely on the all-or-nothing paradigm: a user receives all the posts from an account s/he follows. A consequence for a user is the risk of flooding, i.e., the number of posts received from all the accounts s/he follows implies a time-consuming scan of her/his feed to find relevant updates that match his interests. To avoid user flooding and to significantly diminish the number of posts to be delivered by the system, the authors propose to introduce filtering on top of microblogging systems. Driven by the shape of the data, the authors designed different filtering structures and compared them analytically as well as experimentally on a Twitter dataset which consists of more than 2.1 million users, 15.7 million tweets and 148.5 million publisher-follower relationships.

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“…Indeed, companies can also profit from such approaches. For instance, Sommer et al (2012) developed an approach to analyze customer sentiments on Twitter. The analysis can lead to a better product development or improved marketing strategies.…”

Section: Introductionmentioning

confidence: 99%

Knowledge-Based Recommendation Systems

Bouraga

Jureta

Faulkner

et al. 2014

International Journal of Intelligent Information Technologies

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Knowledge-Base Recommendation (or Recommender) Systems (KBRS) provide the user with advice about a decision to make or an action to take. KBRS rely on knowledge provided by human experts, encoded in the system and applied to input data, in order to generate recommendations. This survey overviews the main ideas characterizing a KBRS. Using a classification framework, the survey overviews KBRS components, user problems for which recommendations are given, knowledge content of the system, and the degree of automation in producing recommendations.

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What is the Conversation About?

Cited by 14 publications

References 29 publications

Trends in Preventive Behaviors Across Countries: An Exploratory Study of English Language Tweets

Trends in Preventive Behaviors Across Countries: An Exploratory Study of English Language Tweets

Filtering Structures for Microblogging Content

Knowledge-Based Recommendation Systems

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