Download In this paper show how social media content can

Mechanism to Measure Reputation of Social Networks using
Reinforcement Learning
Literature Review
In this research paper examine whether public online traces can be used for inferring the
reputation of a person as perceived by others in relation to trustworthiness, influence, expertise, and
impact. The paper shows that to examine the relationship of this list with the ground truth used the
popular normalized Discounted Cumulative Gain (NDCG) measure, in order to locate peers used
SONAR familiarity list and the s/m (selected/mentioned) score. The advantage of this measure lies in
its simplicity and intuitiveness. In our paper going to understand of reputation in social media using
reinforcement learning. [1]
In this paper, a reputation management system is proposed which measures the reputation of a
given company by using the social media data, particularly tweets of Twitter. The proposed method in
this paper is based on N-gram learning approach, which consist of two steps. Train step and Test step.
In this paper evaluation of the proposed method indicates that this method has a better efficiency and
performance in terms of recall and precision compared to Neural Network and Bayesian method. It
from this only discuss about the twitter social network. [2]
In this paper show how social media content can be used to measure the online reputation of a
company. Presents an open platform that uses a sentiment analysis algorithm on twitter traffic to
monitor the real time evolution of company reputation. There are three common basic approaches: fulltext machine learning, linguistic analysis and lexicon-based methods. The sentimental classification in
machine learning approach is based on two steps. First, a training data set is created by manually coding
a set of sentences according to their sentiment. Then, an algorithm for automatically detection of the
sentiment is trained according to the previous classification. The resulting algorithm is then able to
detect and classify new objects according to their sentiment polarity or valence. The linguistic analysis
is inferring the sentiment valence of a text based on its grammatical structure. Linguistic analysis
attempts to identify superlatives, negations, context and idioms as part of the prediction process.
Presents a tool devoted to monitor and visualize online company reputation based on real-time Twitter
stream. Through this paper it doesn’t talk about different sources of data, network dimension and a
broader concept of reputation. [3]
This study analyzes the threat of social media to the corporate reputation that can be damaged
by three different actors. the customer, the employee and the corporation itself. It reviews in the
literature about the impact of each of these actors and assess the findings by means of real-life cases.
Based on the findings, social media reputation threats are discussed in light of corporate response
strategies. But in here it mainly focuses on social media activities of business to customer companies.
The companies analyzed in this literature review mostly shut down their social media activities, ignored
customer complaints or responded reactively by apologizing after the crises occurred. [4]
The use of previous direct interactions is probably the best way to calculate a reputation but,
unfortunately this information is not always available. In this paper we present a reputation system that
takes advantage, among other things, of social relations between agents to overcome this problem. The
system has also a hierarchical ontology structure that allows to consider several types of reputation at
the same time. The combination of complementary methods that use different aspects of the interaction
and social relations, allows the agent to calculate reputation values at different stages of its knowledge
of the society. In this paper reputation is presented using three reputation aspects as Witness reputation,
Neighborhood reputation and System reputation using Artificial Intelligence. In our work it is done by
using reinforcement learning. [5]
In this paper, propose a probabilistic graphical model to collectively measure reputations of
entities in social networks. By collecting and analyzing large amount of user activities on Facebook,
this model can effectively and efficiently rank entities, such as presidential candidates, professional
sport teams, musician bands, and companies, based on their social reputation. This model not only
captures the network information but also includes the semantic information from users in terms of the
comments they make. The biggest advantages of this model is that it reduces the biased effect from a
single user and a single comment. It collectively infers the brand reputation and user positivity. To
efficiently perform the inference, implements a parallelized block-based MCMC algorithm due to the
existence of many conditional probability independencies in the model. Experiments conducted on a
large amount of data from Facebook, and compared brand reputations with two existing ranking
systems. In our approach going to develop using reinforcement learning algorithms. [6]
In this paper they have propose a game theory approach with both a finite and infinite horizon.
The model presented here helps us to evaluate the impact of social media on a firm’s reputation. It also
highlights the important parameters of a firm’s reputation in this new digital era. Based on this model
able define concepts such as reaction time, recovery time and level of reputation. [7]
References
[1] Michal Jacovi, Ido Guy, Shiri Kremer-Davidson, Sara Porat, Netta Aizenbud-Reshef, “Inferring
Reputation from Social Media in the Enterprise”, Baltimore, MD, USA, 2014.
[2] Hossein Shad Manaman, Shahram jamali, Abolfazl Aleahmad, “Online reputation measurement of
companies based on user-generated content in online social networks”, Department of computer,
Khalkhal Branch, Islamic Azad University, Khalkhal, Iran, 2016.
[3] Elanor Colleoni, Adam Arvidsson, Lars K. Hansen, Andrea Marchesini, “Monitoring Corporate
Reputation in Social Media using Real-time Sentiment Analysis”,
The 15th International Conference on Corporate Reputation: Navigating the Reputation Economy,
New Orleans, USA, May 18. - 20. 2011.
[4] Ines Schulze Horn, Torben Taros, Sven Dirkes, Lucas Huer, Maximilian Rose, Raphael
Tietmeyer, Efthymios Constantinides, “Business Reputation and Social Media”, Faculty of
Behavioural, Management and Social Sciences, University of Twente, Enschede, The Netherlands,
2015.
[5] Jordi Sabater, Carles Sierra, “Reputation and Social Network Analysis in MultiAgent Systems”,
Artificial Intelligence Research Institute, Spanish Scientific Research Council, Bellaterra, Catalonia,
Spain. 2002.
[6] Kunpeng Zhang, Doug Downey, Zhengzhang Chen, Yusheng Xie, Yu Cheng,Ankit Agrawal, Weikeng Liao, Alok Choudhary, “A Probabilistic Graphical Model for Brand Reputation Assessment in
Social Networks”, Northwestern University,Evanston, 2013.
[7] Thierry Warin, Nathalie de Marcellis-Warin, William Sanger, Bertrand Nembot, Venus Hossein
Mirza, “Corporate Reputation and Social Media, A Game Theory Approach”, 2013