19,924 research outputs found
Ranking of high-value social audiences on Twitter
Even though social media offers plenty of business opportunities, for a company to identify the right audience from the massive amount of social media data is highly challenging given finite resources and marketing budgets. In this paper, we present a ranking mechanism that is capable of identifying the top-k social audience members on Twitter based on an index. Data from three different Twitter business account owners were used in our experiments to validate this ranking mechanism. The results show that the index developed using a combination of semi-supervised and supervised learning methods is indeed generic enough to retrieve relevant audience members from the three different data sets. This approach of combining Fuzzy Match, Twitter Latent Dirichlet Allocation and Support Vector Machine Ensemble is able to leverage on the content of account owners to construct seed words and training data sets with minimal annotation efforts. We conclude that this ranking mechanism has the potential to be adopted in real-world applications for differentiating prospective customers from the general audience and enabling market segmentation for better business decision making
Partisan Asymmetries in Online Political Activity
We examine partisan differences in the behavior, communication patterns and
social interactions of more than 18,000 politically-active Twitter users to
produce evidence that points to changing levels of partisan engagement with the
American online political landscape. Analysis of a network defined by the
communication activity of these users in proximity to the 2010 midterm
congressional elections reveals a highly segregated, well clustered partisan
community structure. Using cluster membership as a high-fidelity (87% accuracy)
proxy for political affiliation, we characterize a wide range of differences in
the behavior, communication and social connectivity of left- and right-leaning
Twitter users. We find that in contrast to the online political dynamics of the
2008 campaign, right-leaning Twitter users exhibit greater levels of political
activity, a more tightly interconnected social structure, and a communication
network topology that facilitates the rapid and broad dissemination of
political information.Comment: 17 pages, 10 figures, 6 table
Like, share, vote
This report explores the potential for social media to support efforts to get out the vote.
Overview
Across Europe, low voter turnout in European and national elections is a growing concern. Many citizens are disengaged from the political process, threatening the health of our democracies. At the same time, the increasingly prominent role that social media plays in our lives and its function as a new digital public space offers new opportunities to reengage non-voters.
This report explores the potential for social media to support efforts to get out the vote. It lays out which groups need to be the focus of voter mobilisation efforts, and makes the case for using social media campaigning as a core part of our voter mobilisation efforts. The research draws on a series of social media voter mobilisation workshops run by Demos with small third sector organisations in six target countries across Europe, as well as expert interviews, literature review and social media analysis.
Having affirmed the need for and utility of social media voter turnout efforts, Like, Share, Vote establishes key principles and techniques for a successful social media campaign: how to listen to the digital discourse of your audience, how to use quizzes and interactive approaches, how to micro-target specific groups and how to coordinate offline events with online campaigns. This report concludes that, with more of our social and political lives taking place online than ever before, failing to use social media to reinvigorate our democracy would be a real missed opportunity
Identifying the high-value social audience from Twitter through text-mining methods
Doing business on social media has become a common practice for many companies these days. While the contents shared on Twitter and Facebook offer plenty of opportunities to uncover business insights, it remains a challenge to sift through the huge amount of social media data and identify the potential social audience who is highly likely to be interested in a particular company. In this paper, we analyze the Twitter content of an account owner and its list of followers through various text mining methods, which include fuzzy keyword matching, statistical topic modeling and machine learning approaches. We use tweets of the account owner to segment the followers and identify a group of high-value social audience members. This enables the account owner to spend resources more effectively by sending offers to the right audience and hence maximize marketing efficiency and improve the return of investment
On the Identification of the Key Factors for a Successful Use of Twitter as a Medium from a Social Marketing Perspective
[EN] Public administrations are organizations whose mission is to serve the interests of society by providing efficient and sustainable services. Much of the information received from public administrations uses social media due to their versatility and capacity to reach a large number of citizens. Among them, Twitter is the most widely used, especially to disseminate messages with a high social content. This type of messages falls within the discipline of social marketing. However, when public administrations use Twitter for social marketing communication, it is not known which factors are the most decisive to achieve the social objective for which they are issued. This article provides an answer to this question, using the Analytic Network Process Multicriteria method to determine which factors matter and how they are interrelated when issuing social marketing messages through Twitter. The result of this research reveals that from the 22 factors analyzed, the most influential from a social marketing point of view are the average age of population, the existence of a strategic communication plan, the number of tweets and the average number of tweets per day, the number of followers, retweets and mentions, as well as the efficiency of the account.Guijarro, E.; Santandreu Mascarell, C.; Blasco-Gallego, B.; CanĂłs-DarĂłs, L.; Babiloni, E. (2021). On the Identification of the Key Factors for a Successful Use of Twitter as a Medium from a Social Marketing Perspective. Sustainability. 13(12):1-15. https://doi.org/10.3390/su13126696S115131
Topicality and Social Impact: Diverse Messages but Focused Messengers
Are users who comment on a variety of matters more likely to achieve high
influence than those who delve into one focused field? Do general Twitter
hashtags, such as #lol, tend to be more popular than novel ones, such as
#instantlyinlove? Questions like these demand a way to detect topics hidden
behind messages associated with an individual or a hashtag, and a gauge of
similarity among these topics. Here we develop such an approach to identify
clusters of similar hashtags by detecting communities in the hashtag
co-occurrence network. Then the topical diversity of a user's interests is
quantified by the entropy of her hashtags across different topic clusters. A
similar measure is applied to hashtags, based on co-occurring tags. We find
that high topical diversity of early adopters or co-occurring tags implies high
future popularity of hashtags. In contrast, low diversity helps an individual
accumulate social influence. In short, diverse messages and focused messengers
are more likely to gain impact.Comment: 9 pages, 7 figures, 6 table
Detecting Sarcasm in Multimodal Social Platforms
Sarcasm is a peculiar form of sentiment expression, where the surface
sentiment differs from the implied sentiment. The detection of sarcasm in
social media platforms has been applied in the past mainly to textual
utterances where lexical indicators (such as interjections and intensifiers),
linguistic markers, and contextual information (such as user profiles, or past
conversations) were used to detect the sarcastic tone. However, modern social
media platforms allow to create multimodal messages where audiovisual content
is integrated with the text, making the analysis of a mode in isolation
partial. In our work, we first study the relationship between the textual and
visual aspects in multimodal posts from three major social media platforms,
i.e., Instagram, Tumblr and Twitter, and we run a crowdsourcing task to
quantify the extent to which images are perceived as necessary by human
annotators. Moreover, we propose two different computational frameworks to
detect sarcasm that integrate the textual and visual modalities. The first
approach exploits visual semantics trained on an external dataset, and
concatenates the semantics features with state-of-the-art textual features. The
second method adapts a visual neural network initialized with parameters
trained on ImageNet to multimodal sarcastic posts. Results show the positive
effect of combining modalities for the detection of sarcasm across platforms
and methods.Comment: 10 pages, 3 figures, final version published in the Proceedings of
ACM Multimedia 201
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