Designing a Social Media Analytics Dashboard for Government Agency
Crisis Communications
- URL: http://arxiv.org/abs/2202.05541v1
- Date: Fri, 11 Feb 2022 10:41:01 GMT
- Title: Designing a Social Media Analytics Dashboard for Government Agency
Crisis Communications
- Authors: Ali Sercan Basyurt, Julian Marx, Stefan Stieglitz, Milad Mirbabaie
- Abstract summary: Government agencies are increasingly turning to social media to use it as a mouthpiece in times of crisis.
Government agencies need tools that support them in analysing social media data for the public good.
This paper presents a design science research approach that guides the development of a social media analytics dashboard for a regional government agency.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Social media have become a valuable source for extracting data about societal
crises and an important outlet to disseminate official information. Government
agencies are increasingly turning to social media to use it as a mouthpiece in
times of crisis. Gaining intelligence through social media analytics, however,
remains a challenge for government agencies, e.g. due to a lack of training and
instruments. To mitigate this shortcoming, government agencies need tools that
support them in analysing social media data for the public good. This paper
presents a design science research approach that guides the development of a
social media analytics dashboard for a regional government agency. Preliminary
results from a workshop and the resulting design of a first prototype are
reported. A user-friendly and responsive design that is secure, flexible, and
quick in use could identified as requirements, as well as information display
of regional discussion statistics, sentiment, and emerging topics.
Related papers
- CrisisSense-LLM: Instruction Fine-Tuned Large Language Model for Multi-label Social Media Text Classification in Disaster Informatics [49.2719253711215]
This study introduces a novel approach to disaster text classification by enhancing a pre-trained Large Language Model (LLM)
Our methodology involves creating a comprehensive instruction dataset from disaster-related tweets, which is then used to fine-tune an open-source LLM.
This fine-tuned model can classify multiple aspects of disaster-related information simultaneously, such as the type of event, informativeness, and involvement of human aid.
arXiv Detail & Related papers (2024-06-16T23:01:10Z) - Knowledge Boundary and Persona Dynamic Shape A Better Social Media Agent [69.12885360755408]
We construct a social media agent based on personalized knowledge and dynamic persona information.
For personalized knowledge, we add external knowledge sources and match them with the persona information of agents, thereby giving the agent personalized world knowledge.
For dynamic persona information, we use current action information to internally retrieve the persona information of the agent, thereby reducing the interference of diverse persona information on the current action.
arXiv Detail & Related papers (2024-03-28T10:01:23Z) - Social Media Harms as a Trilemma: Asymmetry, Algorithms, and Audacious
Design Choices [0.0]
Social media has expanded in its use, and reach, since the inception of early social networks in the early 2000s.
We argue that as information (eco)systems, social media sites are vulnerable from three aspects.
We will unpack suggestions from various allied disciplines in untangling the 3A's above.
arXiv Detail & Related papers (2023-04-28T08:12:38Z) - Analyzing social media with crowdsourcing in Crowd4SDG [1.1403672224109254]
This study presents an approach that provides flexible support for analyzing social media, particularly during emergencies.
The focus is on analyzing images and text contained in social media posts and a set of automatic data processing tools for filtering, classification, and geolocation of content.
Such support includes both feedback and suggestions to configure automated tools, and crowdsourcing to gather inputs from citizens.
arXiv Detail & Related papers (2022-08-04T14:42:20Z) - Adherence to Misinformation on Social Media Through Socio-Cognitive and
Group-Based Processes [79.79659145328856]
We argue that when misinformation proliferates, this happens because the social media environment enables adherence to misinformation.
We make the case that polarization and misinformation adherence are closely tied.
arXiv Detail & Related papers (2022-06-30T12:34:24Z) - Disaster Tweets Classification using BERT-Based Language Model [6.700873164609009]
Social networking services have become an important communication channel in time emergency.
The aim of this study is to create a machine learning language model that is able to investigate if a person or area was in danger or not.
arXiv Detail & Related papers (2022-01-31T10:25:29Z) - Towards Socially Intelligent Agents with Mental State Transition and
Human Utility [97.01430011496576]
We propose to incorporate a mental state and utility model into dialogue agents.
The hybrid mental state extracts information from both the dialogue and event observations.
The utility model is a ranking model that learns human preferences from a crowd-sourced social commonsense dataset.
arXiv Detail & Related papers (2021-03-12T00:06:51Z) - Clustering of Social Media Messages for Humanitarian Aid Response during
Crisis [47.187609203210705]
We show that recent advances in Deep Learning and Natural Language Processing outperform prior approaches for the task of classifying informativeness.
We extend these methods to two sub-tasks of informativeness and find that the Deep Learning methods are effective here as well.
arXiv Detail & Related papers (2020-07-23T02:18:05Z) - Sense-Giving Strategies of Media Organisations in Social Media Disaster
Communication: Findings from Hurricane Harvey [0.0]
This study investigates the communication strategies of media organisations in extreme events.
A Twitter dataset consisting of 9,414,463 postings was collected during Hurricane Harvey in 2017.
Social network and content analysis methods were applied to identify media communication approaches.
arXiv Detail & Related papers (2020-04-18T09:37:17Z) - Leveraging Multi-Source Weak Social Supervision for Early Detection of
Fake News [67.53424807783414]
Social media has greatly enabled people to participate in online activities at an unprecedented rate.
This unrestricted access also exacerbates the spread of misinformation and fake news online which might cause confusion and chaos unless being detected early for its mitigation.
We jointly leverage the limited amount of clean data along with weak signals from social engagements to train deep neural networks in a meta-learning framework to estimate the quality of different weak instances.
Experiments on realworld datasets demonstrate that the proposed framework outperforms state-of-the-art baselines for early detection of fake news without using any user engagements at prediction time.
arXiv Detail & Related papers (2020-04-03T18:26:33Z) - Curating Social Media Data [0.0]
We propose a data curation pipeline, namely CrowdCorrect, to enable analysts cleansing and curating social data.
Our pipeline provides an automatic feature extraction from a corpus of social media data using existing in-house tools.
The implementation of this pipeline also includes a set of tools for automatically creating micro-tasks to facilitate the contribution of crowd users in curating the raw data.
arXiv Detail & Related papers (2020-02-21T10:07:15Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.