Relevance & Research Question: The research topic is tweeting strategies of Finnish CEOs. The research question is “what kind of strategies do the most followed Finnish CEOs employ to reach a larger audience”. CEOs are increasingly seen as the face of the company they represent and their personal messages can be more effective than the official company messages. CEOs can utilize Twitter for spreading their own message and creating a positive image of themselves and their company. For a tweeting CEO a large follower base is important, as this means a wider audience for their message. Large numbers of followers can also create an image of the CEO as “someone worth listening to”, thus increasing the influence of their message.

Methods & Data: Research data consists of tweets gathered from the public accounts of 133 CEOs of Finnish publicly listed companies and companies on the list of 500 largest companies in Finland. The data was gathered during a seven-month period using Twitter Archiving Google Sheet (TAGS). Research method combines qualitative and quantitative analysis, where tweets are manually categorized by content into categories (topic, function, tone, authenticity and type of interaction). Finally, quantitative analysis is used to determine which categories are the most common among the CEOs with largest follower bases.

Results: Results show the most followed CEOs use a variety of functions to establish a positive image of themselves in Twitter. The most common functions are personal branding, company branding and interaction with stakeholders. The overall tone of these tweets is neutral to positive.

Added Value: So far there has been little research into the Twitter use of CEOs. This research brings added value to the field of leadership communication by providing insight on the communication strategies CEOs employ to gain success in this new platform. So far online communication research has largely focused on political communication in Twitter. This study brings new insight into the field by analyzing how Twitter is used for communication by a different user group. It also benefits business leaders by providing knowledge about tweeting strategies connected to a successful Twitter presence.

Relevance & Research Question:

Of the 709 members of the German Bundestag 524 have Twitter accounts, most of which are active. How do members of the Bundestag tweet about one another? What factions can be found? How does party affiliation align with network clustering? Which parties are most central, which are most peripheral? Which politicians are most influential? How do vertex centrality metrics align with party influence?

Methods & Data:

Social network and content analysis is applied to tweets published by members of the current Bundestag over 10-day time frames. The data is drawn from the free Twitter API via NodeXL Pro. Overall network metrics, cluster analysis and vertex centrality metrics are considered to measure, map and compare the changing networks of tweets to reveal the hidden patterns of affiliation and conflict.

Results:

We find that internal party cohesion can be measured and varies significantly with Bündnis90/Die Grünen most internally cohesive and CDU/CSU least internally interconnected. The inter party network shows the AfD is least connected and most peripheral within the Bundestag, while content analysis shows that #AfD is one of the most frequently used hashtag across party lines.

The clustering algorithm (Clauset-Newman-Moore) detects different sets of party coalitions when considering different layers of network data and also different time frames: Black-Red (CDU/CSU and SPD), Black-Yellow (CDU/CSU and FDP), Red-Red (SPD and Die Linke), Green-Red (Bündnis90/Die Grünen and Die Linke).

Added Value:

The network approach highlights the importance of using patterns of connection to create clusters of content for analysis in contrast to a single “bag of words” approach. Social media network analysis of content from elected officials can reveal implicit coalitions and highlight leaders and key topics and linked web resources. This method complements traditional methods by offering a comprehensive overview of the structure of connections.

Relevance & Research Question: There is considerable concern about the propagation of disinformation through social media, particularly for political purposes. 'Organic reach' has been found to be important in the propagation of disinformation on social networks. This is the phenomenon whereby users extend the audience for a piece of information. Interacting with it, or sharing it, greatly increases the number of people the information reaches. If some types of people (e.g. those with specific personality profiles) are more likely to propagate misinformation, there is potential for those seeking to disseminate disinformation to extend its reach by selective targeting

Methods & Data: In an online study, 357 Facebook users completed personality and Risk Propensity scales, and rated their likelihood of interacting in various ways with a message posted by either a trustworthy or untrustworthy source. A measure of overall organic reach was derived from these ratings.

Results: Message source impacted on overall organic reach, with messages from trusted sources being rated as more likely to be propagated. Risk propensity did not influence reach. However, low scores on trait Agreeableness predicted greater likelihood of interacting with a message.

Added Value: Findings provide preliminary evidence that both message source and recipient characteristics can potentially influence the spread of disinformation. The scope for those seeking to disseminate disinformation to leverage this effect is a cause for concern.