scholarly journals Argument Mining on Twitter: A survey

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Robin Schaefer ◽  
Manfred Stede
Keyword(s):  
Social Media ◽  
State Of The Art ◽  
The State ◽  
Corpus Annotation ◽  
Review Current

Abstract In the last decade, the field of argument mining has grown notably. However, only relatively few studies have investigated argumentation in social media and specifically on Twitter. Here, we provide the, to our knowledge, first critical in-depth survey of the state of the art in tweet-based argument mining. We discuss approaches to modelling the structure of arguments in the context of tweet corpus annotation, and we review current progress in the task of detecting argument components and their relations in tweets. We also survey the intersection of argument mining and stance detection, before we conclude with an outlook.

IT for Peace? Fighting Against Terrorism in Social Media – An Explorative Twitter Study

i-com ◽  
2017 ◽  
Vol 16 (2) ◽  
pp. 181-193 ◽  
Author(s):  
Christian Reuter ◽  
Katja Pätsch ◽  
Elena Runft
Keyword(s):  
Social Media ◽  
Information Systems ◽  
Empirical Study ◽  
State Of The Art ◽  
The State ◽  
The Internet ◽  
New Members ◽  
Starting Point ◽  
The Media

AbstractThe Internet and especially social media are not only used for supposedly good purposes. For example, the recruitment of new members and the dissemination of ideologies of terrorism also takes place in the media. However, the fight against terrorism also makes use of the same tools. The type of these countermeasures, as well as the methods, are covered in this work. In the first part, the state of the art is summarized. The second part presents an explorative empirical study of the fight against terrorism in social media, especially on Twitter. Different, preferably characteristic forms are structured within the scope with the example of Twitter. The aim of this work is to approach this highly relevant subject with the goal of peace, safety and safety from the perspective of information systems. Moreover, it should serve following researches in this field as basis and starting point.

scholarly journals GIF Video Sentiment Detection Using Semantic Sequence

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Dazhen Lin ◽  
Donglin Cao ◽  
Yanping Lv ◽  
Zheng Cai
Keyword(s):  
Social Media ◽  
State Of The Art ◽  
The State ◽  
Middle Level ◽  
Semantic Gap ◽  
Classification Methods ◽  
Detection Approach ◽  
Semantic Concepts ◽  
Sentiment Detection ◽  
Media Applications

With the development of social media, an increasing number of people use short videos in social media applications to express their opinions and sentiments. However, sentiment detection of short videos is a very challenging task because of the semantic gap problem and sequence based sentiment understanding problem. In this context, we propose a SentiPair Sequence based GIF video sentiment detection approach with two contributions. First, we propose a Synset Forest method to extract sentiment related semantic concepts from WordNet to build a robust SentiPair label set. This approach considers the semantic gap between label words and selects a robust label subset which is related to sentiment. Secondly, we propose a SentiPair Sequence based GIF video sentiment detection approach that learns the semantic sequence to understand the sentiment from GIF videos. Our experiment results on GSO-2016 (GIF Sentiment Ontology) data show that our approach not only outperforms four state-of-the-art classification methods but also shows better performance than the state-of-the-art middle level sentiment ontology features, Adjective Noun Pairs (ANPs).

Historical corpus annotation

2017 ◽  
Author(s):  
Gard B. Jenset ◽  
Barbara McGillivray
Keyword(s):  
Semantic Analysis ◽  
State Of The Art ◽  
The State ◽  
Point Of View ◽  
Corpus Annotation ◽  
Technical Point ◽  
Historical Texts ◽  
Linguistic Annotation ◽  
Xml Technologies ◽  
Different Levels

Chapter 4 explains the concept and process of annotation for historical corpora, from a theoretical, practical, and technical point of view, and discusses the challenges presented by historical texts. We introduce basic terminology for XML technologies and corpus metadata, and we describe the different levels of linguistic annotation, from spelling normalization to morphological, syntactic, and semantic analysis, and briefly present the state of the art for historical corpora and treebanks. We cover annotation schemes and standards and illustrate the main concepts in corpus annotation with an example from LatinISE, a large annotated Latin corpus.

scholarly journals Interaction Strength Analysis to Model Retweet Cascade Graphs

2020 ◽  
Vol 10 (23) ◽  
pp. 8394
Author(s):  
Paola Zola ◽  
Guglielmo Cola ◽  
Michele Mazza ◽  
Maurizio Tesconi
Keyword(s):  
Social Media ◽  
Information Diffusion ◽  
State Of The Art ◽  
Interaction Strength ◽  
Diffusion Path ◽  
The State ◽  
Strength Analysis ◽  
Information Propagation ◽  
Original Message ◽  
Graph Generation

Tracking information diffusion is a non-trivial task and it has been widely studied across different domains and platforms. The advent of social media has led to even more challenges, given the higher speed of information propagation and the growing impact of social bots and anomalous accounts. Nevertheless, it is crucial to derive a trustworthy information diffusion graph that is capable of highlighting the importance of specific nodes in spreading the original message. The paper introduces the interaction strength, a novel metric to model retweet cascade graphs by exploring users’ interactions. Initial findings showed the soundness of the approaches based on this new metric with respect to the state-of-the-art model, and its ability to generate a denser graph, revealing crucial nodes that participated in the retweet propagation. Reliable retweet graph generation will enable a better understanding of the diffusion path of a specific tweet.

scholarly journals Understanding Media Communication: On the Significance of Iconic Thinking for a Praxeological Model of Communication

SAGE Open ◽  
2020 ◽  
Vol 10 (3) ◽  
pp. 215824402095206
Author(s):  
Aglaja Przyborski ◽  
Thomas Slunecko
Keyword(s):  
Qualitative Research ◽  
Social Media ◽  
Social Research ◽  
State Of The Art ◽  
The State ◽  
Self Presentation ◽  
Media Communication ◽  
Picture Analysis

This article outlines the state of the art in picture analysis as it has been developed in the trajectory of reconstructive methodology. Analyzing pictures in their own right—that is, by adhering to the particular affordances of the medium “picture”—has strong implications for qualitative research some of which are discussed in this article. With regard to content, this discussion revolves around questions pertaining to bodily self-presentation in mass and social media. On this basis, the article concludes with a praxeological model of communication that offers a guideline for social research which is clued-up as to its own media and, thus, takes into account that meaning in pictures is constructed differently than meaning in language.

Altmetrics: an analysis of the state-of-the-art in measuring research impact on social media

2016 ◽  
Vol 109 (2) ◽  
pp. 1117-1166 ◽  
Author(s):  
Mojisola Erdt ◽  
Aarthy Nagarajan ◽  
Sei-Ching Joanna Sin ◽  
Yin-Leng Theng
Keyword(s):  
Social Media ◽  
State Of The Art ◽  
Research Impact ◽  
The State

scholarly journals Rumour prevention in social networks with layer 2 blockchains

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Subhasis Thakur ◽  
John G. Breslin
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Social Network ◽  
State Of The Art ◽  
Privacy Preserving ◽  
The State ◽  
Media Content ◽  
The Social ◽  
Layer 2 ◽  
Art Methods

AbstractSocial bots can cause social, political, and economical disruptions by spreading rumours. The state-of-the-art methods to prevent social bots from spreading rumours are centralised and such solutions may not be accepted by users who may not trust a centralised solution being biased. In this paper, we developed a decentralised method to prevent social bots. In this solution, the users of a social network create a secure and privacy-preserving decentralised social network and may accept social media content if it is sent by its neighbour in the decentralised social network. As users only choose their trustworthy neighbours from the social network to be part of its neighbourhood in the decentralised social network, it prevents the social bots to influence a user to accept and share a rumour. We prove that the proposed solution can significantly reduce the number of users who are share rumour.

scholarly journals Location Analysis for Arabic COVID-19 Twitter Data Using Enhanced Dialect Identification Models

2021 ◽  
Vol 11 (23) ◽  
pp. 11328
Author(s):  
Nader Essam ◽  
Abdullah M. Moussa ◽  
Khaled M. Elsayed ◽  
Sherif Abdou ◽  
Mohsen Rashwan ◽  
...  
Keyword(s):  
Social Media ◽  
Language Processing ◽  
State Of The Art ◽  
Weighted Average ◽  
Arabic Language ◽  
Arab Countries ◽  
The State ◽  
Identification Accuracy ◽  
Language Models ◽  
Twitter Data

The recent surge of social media networks has provided a channel to gather and publish vital medical and health information. The focal role of these networks has become more prominent in periods of crisis, such as the recent pandemic of COVID-19. These social networks have been the leading platform for broadcasting health news updates, precaution instructions, and governmental procedures. They also provide an effective means for gathering public opinion and tracking breaking events and stories. To achieve location-based analysis for social media input, the location information of the users must be captured. Most of the time, this information is either missing or hidden. For some languages, such as Arabic, the users’ location can be predicted from their dialects. The Arabic language has many local dialects for most Arab countries. Natural Language Processing (NLP) techniques have provided several approaches for dialect identification. The recent advanced language models using contextual-based word representations in the continuous domain, such as BERT models, have provided significant improvement for many NLP applications. In this work, we present our efforts to use BERT-based models to improve the dialect identification of Arabic text. We show the results of the developed models to recognize the source of the Arabic country, or the Arabic region, from Twitter data. Our results show 3.4% absolute enhancement in dialect identification accuracy on the regional level over the state-of-the-art result. When we excluded the Modern Standard Arabic (MSA) set, which is formal Arabic language, we achieved 3% absolute gain in accuracy between the three major Arabic dialects over the state-of-the-art level. Finally, we applied the developed models on a recently collected resource for COVID-19 Arabic tweets to recognize the source country from the users’ tweets. We achieved a weighted average accuracy of 97.36%, which proposes a tool to be used by policymakers to support country-level disaster-related activities.

scholarly journals Representativeness-aware Aspect Analysis for Brand Monitoring in Social Media

2017 ◽  
Author(s):  
Lizi Liao ◽  
Xiangnan He ◽  
Zhaochun Ren ◽  
Liqiang Nie ◽  
Huan Xu ◽  
...  
Keyword(s):  
Social Media ◽  
Information Extraction ◽  
General Public ◽  
Optimization Problem ◽  
State Of The Art ◽  
The State ◽  
Free Text ◽  
Submodular Optimization ◽  
Aspect Extraction ◽  
The Impact

Owing to the fast-responding nature and extreme success of social media, many companies resort to social media sites for monitoring their brands’ reputation and the opinions of general public. To help companies monitor their brands, in this work, we delve into the task of extracting representative aspects and posts from users’ free-text posts in social media. Previous efforts have treated it as a traditional information extraction task, and forgo the specific properties of social media, such as the possible noise in user generated posts and the varying impacts; In contrast, we extract aspects by maximizing their representativeness, which is a new notion defined by us that accounts for both the coverage of aspects and the impact of posts. We formalize it as a submodular optimization problem, and develop a FastPAS algorithm to jointly select representative posts and aspects. The FastPAS algorithm optimizes parameters in a greedy way, which is highly efficient and can reach a good solution with theoretical guarantees. We perform extensive experiments on two datasets, showing that our method outperforms the state-of-the-art aspect extraction and summarization methods in identifying representative aspects.

Sign in / Sign up

Export Citation Format

Share Document