Sample PHD Dynamic Theory Literature Review

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Political Brands: Opinion Dynamics Theory and its Impact on Behavioural Intentions

At the heart of opinion dynamics theory (ODT) is the question of whether and how agents come to change their opinions based on external influences, including other people (Hoyts et al., 2001). Unlike social impact theory, which separates the target from the group, ODT considers an individual to be an integral part of interacting group agents (Latane, 1996). This study focuses on building a positivist ontological position to understand the impact of human-machine communication (HMC) – particularly chatbots – on human behavior’s intentionality towards political brands (De Angeli et al., 2001; Ferris, Fedor, and King, 1994).

“Political brands” have a significant and large-scale impact on the political opinions and behavioural intentions of agents (Black and Veloutsou, 2017), and have come to play a strong role in impacting favourable attitudes and behaviours towards a particular ideology or personality. Briefly here and in more detail to follow, according to Howard and Kollanyi (2016), the majority of chatbots are designed to manipulate public opinion and have been used to redirect the attention of users from certain issues based on manipulative tactics.

Methods

This study included a comprehensive literature search of opinion dynamics and behavioural intentions pulled from academic journals with high impact factors (i.e. 3, 4, and 4*).[1] Articles were collected through the following databases: ScienceDirect, EBSCOhost, Springer Link, Springer Open, Emeralds Insight, SAGE, and Google Scholar.

The databases yielded studies that presented a link between opinion dynamics and interaction with political brands concerning behavioural intentions.

The keywords used for searching articles included; opinion theory, social impact theory, chatbots, behavioural intentions, and political brands. Boolean operator ‘AND’ was used to find the articles with the keywords mentioned above.

The search strategy was further refined to identify articles that critically analysed divergent views on opinion dynamics and social impact theories related to behavioural intentions and political brands.

Although various research and academic articles emerged, those selected for inclusion implicitly and/or explicitly focused on opinion dynamics and social impact theory to understand behavioural intentions concerning political brands.

Ultimately, the data extracted from the articles was coded based on themes and the likelihood of supporting the development of conceptual frameworks.

Findings

Hegselmann and Krause (2002) focused on how opinions form within interacting groups and investigated models for dynamics of continuous opinions. Continuous opinion, in simpler terms, represents the continually changing opinion of the participants based on the stimuli or the situation they were presented with; thus resulting in change in their behaviour and opinion.

They concluded that most agents do not share or disregard other people’s opinions. Rather, we show an interest in other people’s opinions (to a certain extent) and this helps in the formation of personal opinions.

This view marks a departure from the classical model of opinion dynamics, which held that the cumulative pressure on individual agents can lead to consensus concerning an initial opinion profile.

The suggestion here is that consensus can only be achieved when agents collectively apply positive weight to individual agents’ views.

Concerning social media platforms, Park (2013) observed that Twitter opinion leaders have influenced individuals’ involvement in political processes, demonstrated through their higher motivations to seek out information, public expressions, and mobilise.

For instance, controversies over Twitter encourage the participants to express themselves while motivating them to seek out the cause of the controversy; thus resulting in their opinion formation.

Diao et al. (2014) focused on the psychological implications of opinions, particularly the social impact on minorities and majorities. It was assessed through the initial density of individuals’ outer actions.

Their findings suggested that a complete consensus is reached when the initial density of a minority and majority is greater than 0.8, which creates a significant imbalance between dominant cultural perspectives and those of minorities.

The significant imbalance element explains the difference in opinions amongst the minorities and majorities; thus the dominant cultural perspective influences the minorities to embrace it, while forming opinions of their own based on their understanding.

Taken together, these findings support the argument that opinion formation results from interacting with other agents. This includes artificial intelligence, particularly on social network platforms like Twitter and Facebook (Ruths and Pfeffer, 2014; Park, 2013; Diao et al., 2014).

Specifically, Twitter users can be influenced by the tweets and re-tweets of other individuals or groups during a typical interaction, perhaps even to the extent that they are influenced to change their attitudes and corresponding behaviours towards a particular subject. Verhagen, Nauta, and Feldberg (2013) suggested negative opinions spread quickly throughout social networking platforms.

Although positive opinions and experiences do not have that same spread, they influence those they reach (Verhagen, Nauta, and Feldberg, 2013). According to Pfeffer, Zorbach, and Carley (2014) more moderate and/or sophisticated opinions, when voiced on social networking sites, do not influence or reach as negative opinions.

It implies that opinions form and are subject to change by context, including political brands (Herbst, 1998). Extending this thinking, Bliuc et al. (2007) suggested that social networking platforms and everyday interactions can positively and negatively influence political brands. I.e., behavioural intentions change with time and through interactions.

The groups an individual most closely associates with exert the strongest influence on their opinions (Watts and Dodds, 2007). Amplifying this view, Diao et al. (2014) suggested that behavioural actions and tendencies arise as reactions to other agents’ opinions within multicultural and multi-agent societies (as confirmed by Senadheera et al., 2017; and Li et al., 2016).

There are rare cases wherein behavioural intentions can occur when individuals do not interact with others (Chong and Druckman, 2007).

Political Brands and Artificial Intelligence

Draxinger (2017) observed that chatbots have been widely used to influence individuals’ opinions about political brands. It was particularly evident in the 2016 United States presidential campaign between Donald Trump and Hillary Clinton, when Facebook was used to change public perceptions (Folkenflik, 2017). According to Lee and Sundar (2013), information on Facebook and other social networks tends to be viewed as credible and authentic. Hence, the systematic defamation of Hillary Clinton throughout Donald Trump’s campaign ultimately influenced the general public to change their views and voting behaviours.

Indeed, chatbots have significantly impacted how the public communicates over digital platforms (Eeuwen, 2017). Companies have come to rely on chatbots to reach their customers, moulding their attitudes and behaviours towards products and services (Eeuwen, 2017). In that same way, political brands frequently use chatbots to create awareness about political parties or ideologies, facilitating changed perceptions towards a party, platform, and/or political personality.

Concluding Remarks

In conclusion, opinion dynamics and social impact theory suggest that social media users’ opinions can have a significant impact on political brands, ideologies, and/or personalities. The literature reviewed for this article points to the influence chatbots have on users’ perceptions. In politics, this results in influencing public perceptions on political brands and promoting behavioural changes.

References

Black, I. and Veloutsou, C., 2017. Working consumers: Co-creation of brand identity, consumer identity and brand community identity. Journal of Business Research, 70, pp.416-429.

Bliuc, A.M., McGarty, C., Reynolds, K. and Muntele, D., 2007. Opinion‐based group membership as a predictor of commitment to political action. European Journal of Social Psychology, 37(1), pp.19-32.

Chong, D. and Druckman, J.N., 2007. A theory of framing and opinion formation in competitive elite environments. Journal of Communication, 57(1), pp.99-118.

De Angeli, A., Johnson, G.I. and Coventry, L., 2001, June. The unfriendly user: exploring social reactions to chatterbots. In Proceedings of The International Conference on Affective Human Factors Design, London (pp. 467-474).

Diao, S.M., Liu, Y., Zeng, Q.A., Luo, G.X. and Xiong, F., 2014. A novel opinion dynamics model based on expanded observation ranges and individuals’ social influences in social networks. Physica A: Statistical Mechanics and its Applications, 415, pp.220-228.

Draxinger, S., 2017. The Generativity of Messaging Platforms: A Case Study on Facebook Messenger and Chatbots. iSChannel, 12(1).

Eeuwen, M.V., 2017. Mobile conversational commerce: messenger chatbots as the next interface between businesses and consumers (Master’s thesis, University of Twente).

Ferris, G.R., Fedor, D.B. and King, T.R., 1994. A political conceptualization of managerial behavior. Human Resource Management Review, 4(1), pp.1-34.

Hegselmann, R. and Krause, U., 2002. Opinion dynamics and bounded confidence models, analysis, and simulation. Journal of artificial societies and social simulation, 5(3).

Herbst, S., 1998. Reading public opinion: How political actors view the democratic process. University of Chicago Press.

Hołyst, J.A., Kacperski, K. and Schweitzer, F., 2001. Social impact models of opinion dynamics. In Annual Reviews Of Computational PhysicsIX (pp. 253-273).

Io, H.N. and Lee, C.B., 2017, December. Chatbots and conversational agents: A bibliometric analysis. In Industrial Engineering and Engineering Management (IEEM), 2017 IEEE International Conference on (pp. 215-219). IEEE.

Kollanyi, B., Howard, P.N. and Woolley, S.C., 2016. Bots and automation over Twitter during the US election. COMPROP Data Memo.

Latané, B., 1996. Dynamic social impact: The creation of culture by communication. Journal of communication, 46(4), pp.13-25.

Lee, J.Y. and Sundar, S.S., 2013. To tweet or retweet? That is the question for health professionals on Twitter. Health Communication, 28(5), pp.509-524.

Park, C.S., 2013. Does Twitter motivate involvement in politics? Tweeting, opinion leadership, and political engagement. Computers in Human Behavior, 29(4), pp.1641-1648.

Pfeffer, J., Zorbach, T. and Carley, K.M., 2014. Understanding online firestorms: Negative word-of-mouth dynamics in social media networks. Journal of Marketing Communications, 20(1-2), pp.117-128.

Ruths, D. and Pfeffer, J., 2014. Social media for large studies of behavior. Science, 346(6213), pp.1063-1064.

Senadheera, V., Warren, M. and Leitch, S., 2017. Social media as an information system: improving the technological agility. Enterprise Information Systems, 11(4), pp.512-533. Herbst, S., 1998. Reading public opinion: How political actors view the democratic process. University of Chicago Press.

Verhagen, T., Nauta, A. and Feldberg, F., 2013. Negative online word-of-mouth: Behavioral indicator or emotional release?. Computers in Human Behavior, 29(4), pp.1430-1440.

Watts, D.J. and Dodds, P.S., 2007. Influentials, networks, and public opinion formation. Journal of consumer research, 34(4), pp.441-458.

Folkenflik, D. 2017. Facebook Scrutinized Over Its Role In 2016’s Presidential Election. Data retrieved from https://www.npr.org/2017/09/26/553661942/facebook-scrutinized-over-its-role-in-2016s-presidential-election

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