Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userChanges in Twitter geolocations: Insights and suggestions for future usage
التغييرات في تويتر الجغرافيين: رؤى واقتراحات للاستخدام في المستقبل
197
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
أصبحت بيانات Twitter مثبتة كمصدر قيم للبيانات لمختلف سيناريوهات التطبيق في السنوات الماضية.بالنسبة للعديد من هذه التطبيقات، من الضروري معرفة المكان الذي تم إرسال مشاركات Twitter (تغريدات) من أو الموقع الذي يشير إليه.كثيرا ما استخدم الباحثون الإحداثيات الدقيقة المقدمة في نسبة مئوية صغيرة من التغريدات، لكن Twitter أزال الخيار لمشاركة هذه الإحداثيات في منتصف عام 2019.علاوة على ذلك، هناك سبب للشك في أن حصة كبيرة من الإحداثيات المقدمة لم تتوافق مع إحداثيات GPS للمستخدم حتى قبل ذلك.في هذه الورقة، نوضح الوضع وتغيير السياسة لعام 2019 وإلقاء الضوء على الخيارات المختلفة التي لا تزال تحصل على معلومات الموقع من التغريدات.نحن نقدم إحصاءات الاستخدام بما في ذلك التغييرات مع مرور الوقت، وتحليل ما إن إزالة الإحداثيات الدقيقة يعني لمختلف مهام البحث الشائعة التي يتم تنفيذها مع بيانات Twitter.أخيرا، نقدم اقتراحات للأبحاث المستقبلية التي تتطلب التغريدات الجغرافية.
Twitter data has become established as a valuable source of data for various application scenarios in the past years. For many such applications, it is necessary to know where Twitter posts (tweets) were sent from or what location they refer to. Researchers have frequently used exact coordinates provided in a small percentage of tweets, but Twitter removed the option to share these coordinates in mid-2019. Moreover, there is reason to suspect that a large share of the provided coordinates did not correspond to GPS coordinates of the user even before that. In this paper, we explain the situation and the 2019 policy change and shed light on the various options of still obtaining location information from tweets. We provide usage statistics including changes over time, and analyze what the removal of exact coordinates means for various common research tasks performed with Twitter data. Finally, we make suggestions for future research requiring geolocated tweets.
References used
https://aclanthology.org/
rate research
Read More
We bring the data from the social networking site Twitter
pages, and then we have worked on cleaning and processing
operation to the text of for the classification process texts retrieved
contain a lot of noise and information is useful for the pr
ocess of
analyzing the views, such as advertisements and links and e-mail
addresses and the presence of many words that do not affect the
general orientation of the text, and then get all the publications in
the Twitter page and what are the comments about each tweets is
intended to know the proportion of supporters and opponents of
this publication.
We apply Naïve Bayes algorithm in classification, we had the
appropriate training, and after passing Posts and comments data
(opinions), we got good results on the ratio of supporters of the
post and the percentage of his opponents.
Emotion detection from social media posts has attracted noticeable attention from natural language processing (NLP) community in recent years. The ways for obtaining gold labels for training and testing of the systems for automatic emotion detection
differ significantly from one study to another, and pose the question of reliability of gold labels and obtained classification results. This study systematically explores several ways for obtaining gold labels for Ekman's emotion model on Twitter data and the influence of the chosen strategy on the manual classification results.
Social media (SM) platforms such as Twitter provide large quantities of real-time data that can be leveraged during mass emergencies. Developing tools to support crisis-affected communities requires available datasets, which often do not exist for lo
w resource languages. This paper introduces Kawarith a multi-dialect Arabic Twitter corpus for crisis events, comprising more than a million Arabic tweets collected during 22 crises that occurred between 2018 and 2020 and involved several types of hazard. Exploration of this content revealed the most discussed topics and information types, and the paper presents a labelled dataset from seven emergency events that serves as a gold standard for several tasks in crisis informatics research. Using annotated data from the same event, a BERT model is fine-tuned to classify tweets into different categories in the multi- label setting. Results show that BERT-based models yield good performance on this task even with small amounts of task-specific training data.
Stance detection (SD) entails classifying the sentiment of a text towards a given target, and is a relevant sub-task for opinion mining and social media analysis. Recent works have explored knowledge infusion supplementing the linguistic competence a
nd latent knowledge of large pre-trained language models with structured knowledge graphs (KGs), yet few works have applied such methods to the SD task. In this work, we first perform stance-relevant knowledge probing on Transformers-based pre-trained models in a zero-shot setting, showing these models' latent real-world knowledge about SD targets and their sensitivity to context. We then train and evaluate new knowledge-enriched stance detection models on two Twitter stance datasets, achieving state-of-the-art performance on both.
Early exit mechanism aims to accelerate the inference speed of large-scale pre-trained language models. The essential idea is to exit early without passing through all the inference layers at the inference stage. To make accurate predictions for down
stream tasks, the hierarchical linguistic information embedded in all layers should be jointly considered. However, much of the research up to now has been limited to use local representations of the exit layer. Such treatment inevitably loses information of the unused past layers as well as the high-level features embedded in future layers, leading to sub-optimal performance. To address this issue, we propose a novel Past-Future method to make comprehensive predictions from a global perspective. We first take into consideration all the linguistic information embedded in the past layers and then take a further step to engage the future information which is originally inaccessible for predictions. Extensive experiments demonstrate that our method outperforms previous early exit methods by a large margin, yielding better and robust performance.
suggested questions
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
