FORA – A fuzzy set based framework for online reputation management

Portmann, Edy; Meier, Andreas; Cudré-Mauroux, Philippe; Pedrycz, Witold

doi:10.1016/j.fss.2014.06.004

Cited by 15 publications

(7 citation statements)

References 29 publications

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“…According to Larkin (2003) and Dowling (2006), reputation is built on trust and belief and on the experiences and opinions of the public (Aula & Mantere 2008;Portmann et al 2014). In the commercial world, reputation appears to have become a "Cinderella" asset.…”

Section: Unpacking Reputation and Online Reputation Managementmentioning

confidence: 99%

“…Organisations need to put procedures and practices in place to manage their reputation and the risks involved as they set out to engage with the wider community on the social web (Jones et al 2015;Resnick 2004). Practices may include a variety of aspects, but for this study these practices, as part of the research objectives, covered the following aspects (Portmann et al 2014), focusing particularly on online conversations:…”

Section: Unpacking Reputation and Online Reputation Managementmentioning

confidence: 99%

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Investigating social media conversations: Towards implementing an online reputation management framework for NPOs

Heerden

Rensburg

2018

COMM

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The main objective of the research reported in this article was to investigate whether a proposed online reputation management framework can be implemented for an NPO to effect stakeholder engagement in social media content and conversations. The research further assessed whether an NPO can afford greater control of the social web by tracking critical comments and negative publicity and by utilising this information to manage its reputation. The latter was achieved by exploring the Facebook page of a South African trade union, Solidarity, to determine the impact of online conversations on the reputation of the NPO, regardless of the content. A case study methodology was used for the research. The research, which departed from an interpretivist tradition, aimed at analysing the online interactions of stakeholders and their impact on the reputation of the organisation. Data was elicited through interviews with administrators and the content of conversations on Facebook. The latter was analysed through content analysis. The outcomes of the research and the ideas presented in this article might assist NPOs with reputation management by implementing the online reputation management framework as a tool to manage and direct their online content and conversations for improved reputation.

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Section: Unpacking Reputation and Online Reputation Managementmentioning

confidence: 99%

Section: Unpacking Reputation and Online Reputation Managementmentioning

confidence: 99%

Investigating social media conversations: Towards implementing an online reputation management framework for NPOs

Heerden

Rensburg

2018

COMM

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“…Rada proposed an approach for CO‐based similarity measure which utilizes the concepts of shortest path between concepts and consideration of their LCA in the same way as our proposed FO‐based similarity measure. Accordingly, in our evaluation we consider Rada measure for calculation of CO‐based distance measure which is defined as follows:

CO_d i s t false(c_{i}, c_{j} false) = false| p a t h false(c_{i}, LCA false(c_{i}, c_{j} false) false| + false| p a t h false(LCA false(c_{i}, c_{j} false), c_{j} false|

web _dist : In order to evaluate distance based on web data, terms (concepts) co‐occurrences in web files is considered, which is a common literature approach . Co‐occurrence refers to the number of times two specific concepts have been appeared concurrently in a same file.…”

Section: A New Approach For Evaluating Fo‐based Similarity Measurementioning

confidence: 99%

Using Fuzzy Ontology to Improve Similarity Assessment: Method and Evaluation

Safaeipour

Zarandi

Bastani

2017

Int. J. Intell. Syst.

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Assessing semantic similarity is a fundamental requirement for many AI applications. Crisp ontology (CO) is one of the knowledge representation tools that can be used for this purpose. Thanks to the development of semantic web, CO‐based similarity assessment has become a popular approach in recent years. However, in the presence of vague information, CO cannot consider uncertainty of relations between concepts. On the other hand, fuzzy ontology (FO) can effectively process uncertainty of concepts and their relations. This paper aims at proposing an approach for assessing concept similarity based on FO. The proposed approach incorporates fuzzy relation composition in combination with an edge counting approach to assess the similarity. Accordingly, proposed measure relies on taxonomical features of an ontology in combination with statistical features of concepts. Furthermore, an evaluation approach for the FO‐based similarity measure named as FOSE is proposed. Considering social network data, proposed similarity measure is evaluated using FOSE. The evaluation results prove the dominance of proposed approach over its respective CO‐based measure.

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“…By using an integrated user-friendly fuzzy rating mechanism, users do not necessarily require any explicit knowledge of knowledge engineering to make the (rating) contribution. Here, the reputation management framework [41] is adopted for the user expertise classifications. Then, for different user expertise levels, we provide fuzzy rating authorization control for appropriate AoFeCSO input, based on the authorization reference illustrated in Table 1.…”

Section: Fuzzy Data Collectionmentioning

confidence: 99%