scholarly journals Semrank

Author(s):  
Jagrati Singh ◽  
Anil Kumar Singh

Popular real-world events often create huge traffic on Twitter including real-time updates of important moments, personal comments, and so on while the event is happening. Most of the users are interested to read the important tweets that possibly include important moments of that event. However, extracting the relevant tweets of any event is a challenging task due to the endless stream of noisy tweets and vocabulary variation problem of social media content. To handle these challenges, the authors introduce a new approach for computing the relative tweet importance based on the concept of the Pagerank algorithm where adjacency matrix of the graph representation of tweets contains semantic similarity matrix based on the word mover's distance measure utilizing Word2Vec word embedding model. The results show that top-ranked tweets generated by the proposed approach are more concise and news-worthy than baseline approaches.

Author(s):  
Jyh-Ren Shieh ◽  
Ching-Yung Lin ◽  
Shun-Xuan Wang ◽  
Ja-Ling Wu

The abundance of Web 2.0 social media in various media formats calls for integration that takes into account tags associated with these resources. The authors present a new approach to multi-modal media search, based on novel related-tag graphs, in which a query is a resource in one modality, such as an image, and the results are semantically similar resources in various modalities, for instance text and video. Thus the use of resource tagging enables the use of multi-modal results and multi-modal queries, a marked departure from the traditional text-based search paradigm. Tag relation graphs are built based on multi-partite networks of existing Web 2.0 social media such as Flickr and Wikipedia. These multi-partite linkage networks (contributor-tag, tag-category, and tag-tag) are extracted from Wikipedia to construct relational tag graphs. In fusing these networks, the authors propose incorporating contributor-category networks to model contributor’s specialization; it is shown that this step significantly enhances the accuracy of the inferred relatedness of the term-semantic graphs. Experiments based on 200 TREC-5 ad-hoc topics show that the algorithms outperform existing approaches. In addition, user studies demonstrate the superiority of this visualization system and its usefulness in the real world.


Author(s):  
Flora S. Tsai

This paper proposes probabilistic models for social media mining based on the multiple attributes of social media content, bloggers, and links. The authors present a unique social media classification framework that computes the normalized document-topic matrix. After comparing the results for social media classification on real-world data, the authors find that the model outperforms the other techniques in terms of overall precision and recall. The results demonstrate that additional information contained in social media attributes can improve classification and retrieval results.


Author(s):  
Jyh-Ren Shieh ◽  
Ching-Yung Lin ◽  
Shun-Xuan Wang ◽  
Ja-Ling Wu

The abundance of Web 2.0 social media in various media formats calls for integration that takes into account tags associated with these resources. The authors present a new approach to multi-modal media search, based on novel related-tag graphs, in which a query is a resource in one modality, such as an image, and the results are semantically similar resources in various modalities, for instance text and video. Thus the use of resource tagging enables the use of multi-modal results and multi-modal queries, a marked departure from the traditional text-based search paradigm. Tag relation graphs are built based on multi-partite networks of existing Web 2.0 social media such as Flickr and Wikipedia. These multi-partite linkage networks (contributor-tag, tag-category, and tag-tag) are extracted from Wikipedia to construct relational tag graphs. In fusing these networks, the authors propose incorporating contributor-category networks to model contributor’s specialization; it is shown that this step significantly enhances the accuracy of the inferred relatedness of the term-semantic graphs. Experiments based on 200 TREC-5 ad-hoc topics show that the algorithms outperform existing approaches. In addition, user studies demonstrate the superiority of this visualization system and its usefulness in the real world.


Author(s):  
Flora S. Tsai

This paper proposes probabilistic models for social media mining based on the multiple attributes of social media content, bloggers, and links. The authors present a unique social media classification framework that computes the normalized document-topic matrix. After comparing the results for social media classification on real-world data, the authors find that the model outperforms the other techniques in terms of overall precision and recall. The results demonstrate that additional information contained in social media attributes can improve classification and retrieval results.


Author(s):  
Ray Surette

In the 1840s, cheap mass-marketed newspapers raised the relationship among the media, crime, and criminal justice to a new level. The intervening history has only strengthened the bonds, and comprehending the nature of the media, crime, and justice relationship has become necessary for understanding contemporary crime and criminal justice policies. The backward law of media crime and criminal justice content, where the rarest real-world events become the most common media content, continues to operate. In the 21st century, the media present backward snapshots of crime and justice in dramatic, reshaped, and marketed narrow slices of the world. Media portraits emphasize rare crimes like homicide, rare courtroom procedures like trials, rare forensic evidence, and rare correctional events like riots and escapes to present a heavily skewed, unrealistic picture. Significantly exacerbating this long-term tendency are new social media. When the evolution of the media is examined, the trend has been toward the creation of a mediated experience that is indistinguishable from a real-world experience. Each step in the evolution of media brought the mediated experience and the actual personally experienced event closer. The world today is the most media-immersed age in history. The shift to new social media from the legacy media of the 20th century was a crucial turning point. The emergence of social media platforms has sped up what had been a slow evolutionary process. The technological ability of media to gather, recycle, and disseminate information has never been faster, and more crime-related media content is available to more people via more venues and in more formats than ever before. In this new mediated world, everyone is wedded to media in some fashion. Whether through the Internet, television, movies, music, video games, or multipurpose social media devices, exposure to media content is ubiquitous. Media provide a broadly shared, common knowledge of society that is independent of occupation, education, ethnicity, and social class. The cumulative result of this ongoing media evolution is that society has become a multimedia environment where content, particularly images, is ubiquitous in the media. Mediated events blot out actual ones, so that media renditions often supplant and conflict with what actually happened. This trend is particularly powerful in crime and justice, where news, entertainment, and advertising combine with new media to construct a largely unchallenged mediated crime and criminal justice reality. The most significant result is that, in this mediated reality, criminal justice policies are generated. What we believe about criminal justice and what we think ought to be done about crime are based on content that has been parsed, filtered, recast, and refined through electronic, digital, visually dominated, multimedia entities. Ironically, while the media are geared toward narrowcasting and the targeting of small, homogenous audiences, media content is constantly reformatted and looped to ultimately reach wide, multiple, and varied audiences. In the end, the media’s criminal justice role cannot be ignored. Until the linkages between media, crime, and justice are acknowledged and better understood, myopic and punitive criminal justice policies will be the norm.


Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4356 ◽  
Author(s):  
Stefan Bosse ◽  
Uwe Engel

Modelling and simulation of social interaction and networks are of high interest in multiple disciplines and fields of application ranging from fundamental social sciences to smart city management. Future smart city infrastructures and management are characterised by adaptive and self-organising control using real-world sensor data. In this work, humans are considered as sensors. Virtual worlds, e.g., simulations and games, are commonly closed and rely on artificial social behaviour and synthetic sensor information generated by the simulator program or using data collected off-line by surveys. In contrast, real worlds have a higher diversity. Agent-based modelling relies on parameterised models. The selection of suitable parameter sets is crucial to match real-world behaviour. In this work, a framework combining agent-based simulation with crowd sensing and social data mining using mobile agents is introduced. The crowd sensing via chat bots creates augmented virtuality and reality by augmenting the simulated worlds with real-world interaction and vice versa. The simulated world interacts with real-world environments, humans, machines, and other virtual worlds in real-time. Among the mining of physical sensors (e.g., temperature, motion, position, and light) of mobile devices like smartphones, mobile agents can perform crowd sensing by participating in question–answer dialogues via a chat blog (provided by smartphone Apps or integrated into WEB pages and social media). Additionally, mobile agents can act as virtual sensors (offering data exchanged with other agents) and create a bridge between virtual and real worlds. The ubiquitous usage of digital social media has relevant impact on social interaction, mobility, and opinion-making, which has to be considered. Three different use-cases demonstrate the suitability of augmented agent-based simulation for social network analysis using parameterised behavioural models and mobile agent-based crowd sensing. This paper gives a rigorous overview and introduction of the challenges and methodologies used to study and control large-scale and complex socio-technical systems using agent-based methods.


2020 ◽  
Vol 44 (1) ◽  
pp. 71-88
Author(s):  
Katharina Groß-Vogt ◽  
Marian Weger ◽  
Matthias Frank ◽  
Robert Höldrich

Abstract Peripheral interaction is a new approach to conveying information at the periphery of human attention in which sound is so far largely underrepresented. We report on two experiments that explore the concept of sonifying information by adding virtual reverberation to real-world room acoustics. First, to establish proof of concept, we used the consumption of electricity in a kitchen to control its reverberation in real time. The results of a second, in-home experiment showed that at least three levels of information can be conveyed to the listeners with this technique without disturbing a main task being performed simultaneously. This number may be increased for sonifications that are less critical.


2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Dhivya Karmegam ◽  
Sivakumar Ramamoorthy ◽  
Bagavandas Mappillairaju

AbstractDuring and just after flash flood, data regarding water extent and inundation will not be available as the traditional data collection methods fail during disasters. Rapid water extent map is vital for disaster responders to identify the areas of immediate need. Real time data available in social networking sites like Twitter and Facebook is a valuable source of information for response and recovery, if handled in an efficient way. This study proposes a method for mining social media content for generating water inundation mapping at the time of flood. The case of 2015 Chennai flood was considered as the disaster event and 95 water height points with geographical coordinates were derived from social media content posted during the flood. 72 points were within Chennai and based on these points water extent map was generated for the Chennai city by interpolation. The water depth map generated from social media information was validated using the field data. The root mean square error between the actual water height data and extracted social media data was ± 0.3 m. The challenge in using social media data is to filter the messages that have water depth related information from the ample amount of messages posted in social media during disasters. Keyword based query was developed and framed in MySQL to filter messages that have location and water height mentions. The query was validated with tweets collected during the floods that hit Mumbai city in July 2019. The validation results confirm that the query reduces the volume of tweets for manual evaluation and in future will aid in mapping the water extent in near real time at the time of floods.


Sign in / Sign up

Export Citation Format

Share Document